[ { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Data were drawn from the Whitehall II study with baseline examination in 1991; follow-up screenings in 1997, 2003, and 2008; and additional disease ascertainment from hospital data and registry linkage on 5318 participants (mean age 54.8 years, 31% women) without depressive symptoms at baseline. Vascular risk was assessed with the Framingham Cardiovascular, Coronary Heart Disease, and Stroke Risk Scores. New depressive symptoms at each follow-up screening were identified by General Health Questionnaire caseness, a Center for Epidemiologic Studies Depression Scale score \u226516, and use of antidepressant medication.", "measurement_extractions": [ { "quantity": "1991", "unit": null, "measured_entity": "baseline examination", "measured_property": null }, { "quantity": "1997, 2003, and 2008", "unit": null, "measured_entity": "follow-up screenings", "measured_property": null }, { "quantity": "5318 participants", "unit": "participants", "measured_entity": "Whitehall II study", "measured_property": null }, { "quantity": "54.8 years", "unit": "years", "measured_entity": "5318 participants", "measured_property": "mean age" }, { "quantity": "31%", "unit": "%", "measured_entity": "5318 participants", "measured_property": "women" }, { "quantity": "\u226516", "unit": null, "measured_entity": "Center for Epidemiologic Studies Depression Scale", "measured_property": "score" } ], "split": "train", "docId": "S0006322312001096-1136", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The Whitehall II study is a prospective cohort study of British civil servants established in 1985 to study associations between risk factors, pathophysiological changes, and clinical disease (22). The target population was all London-based office staff, aged 35 to 55, working in 20 civil service departments on recruitment to the study in 1985 to 1988 (phase 1). With a response of 73%, the cohort consisted of 10,308 employees (6895 men and 3413 women). Since the phase 1 medical examination, follow-up examinations have taken place approximately every 5 years: phase 3 (1991 to 1993, n = 8815); phase 5 (1997 to 1999, n = 7870); phase 7 (2003 to 2004, n = 6967); and phase 9 (2008 to 2009, n = 6761). Phase 3 was the first time that all components of the Framingham risk algorithms were measured, making it the baseline for the analyses we report here. We use data from three screening cycles: from phase 3 to phase 5, from phase 5 to phase 7, and from phase 7 to phase 9 with phases 3, 5, and 7 providing baseline measures of vascular risk to assess incidence of depressive symptoms at phases 5, 7, and 9 in the three data cycles, respectively.", "measurement_extractions": [ { "quantity": "1985", "unit": null, "measured_entity": "Whitehall II study", "measured_property": "established" }, { "quantity": "35 to 55", "unit": null, "measured_entity": "target population", "measured_property": "aged" }, { "quantity": "20", "unit": null, "measured_entity": "civil service departments", "measured_property": null }, { "quantity": "1985 to 1988", "unit": null, "measured_entity": "target population", "measured_property": "recruitment" }, { "quantity": "73%", "unit": null, "measured_entity": "cohort", "measured_property": "response" }, { "quantity": "10,308 employees", "unit": "employees", "measured_entity": "cohort", "measured_property": null }, { "quantity": "6895 men", "unit": "men", "measured_entity": "cohort", "measured_property": null }, { "quantity": "3413 women", "unit": "women", "measured_entity": "cohort", "measured_property": null }, { "quantity": "approximately every 5 years", "unit": "years", "measured_entity": "follow-up examinations", "measured_property": "taken place" }, { "quantity": "1991 to 1993", "unit": null, "measured_entity": "phase 3", "measured_property": null }, { "quantity": "8815", "unit": null, "measured_entity": "phase 3", "measured_property": "n" }, { "quantity": "1997 to 1999", "unit": null, "measured_entity": "phase 5", "measured_property": null }, { "quantity": "7870", "unit": null, "measured_entity": "phase 5", "measured_property": "n" }, { "quantity": "2003 to 2004", "unit": null, "measured_entity": "phase 7", "measured_property": null }, { "quantity": "6967", "unit": null, "measured_entity": "phase 7", "measured_property": "n" }, { "quantity": "2008 to 2009", "unit": null, "measured_entity": "phase 9", "measured_property": null }, { "quantity": "6761", "unit": null, "measured_entity": "phase 9", "measured_property": "n" }, { "quantity": "three", "unit": null, "measured_entity": "screening cycles", "measured_property": null }, { "quantity": "three", "unit": null, "measured_entity": "data cycles", "measured_property": null } ], "split": "train", "docId": "S0006322312001096-1177", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We used standard operating protocols to measure risk factors for the Framingham general CVD risk score (sex, age, diabetes, smoking, treated and untreated systolic blood pressure, total cholesterol, high-density lipoprotein [HDL] cholesterol), CHD risk score (sex, age, diabetes, smoking, systolic and diastolic blood pressure, total cholesterol, HDL cholesterol), and stroke risk score (sex, age, systolic blood pressure, diabetes, smoking, prior cardiovascular disease, atrial fibrillation, left-ventricular hypertrophy, use of hypertensive medications) at the baseline of each data cycle, i.e., phases 3, 5, and 7 (14\u201317). Venous blood was taken in the fasting state or at least 5 hours after a light, fat-free breakfast. Serum for lipid analyses was refrigerated at \u22124\u00b0C and assayed within 72 hours. Cholesterol was measured with the use of a Cobas Fara centrifugal analyzer (Roche Diagnostics System, Nutley, New Jersey). High-density lipoprotein cholesterol was measured by precipitating non-HDL cholesterol with dextran sulfate-magnesium chloride using a centrifuge and measuring cholesterol in the supernatant. Participants underwent an oral glucose tolerance test and new venous blood samples were taken at 2 hours postadministration of a 75 g glucose solution. Blood glucose was measured using the glucose oxidase method on a YSI MODEL 2300 STAT PLUS Analyzer (YSI Corporation, Yellow Springs, Ohio; mean coefficient of variation: 1.4%\u20133.1%). Diabetes was defined by fasting glucose \u22657.0 mmol/L or 2-hour postload glucose \u226511.1 mmol/L, reported doctor diagnosed diabetes, or use of diabetes medication (24). We measured systolic and diastolic blood pressure twice in the sitting position after 5 minutes rest with a Hawksley random-zero sphygmomanometer (phases 3 and 5) (Lynjay Services Ltd., Worthing, United Kingdom) and OMRON HEM 907 (phase 7) (Omron, Milton Keynes, United Kingdom), the average of two readings used in the analysis. Atrial fibrillation was identified on the Glasgow 12-lead electrocardiogram analysis program combined with manual review (Prof. P. Macfarlane, University of Glasgow, United Kingdom). Limb lead electrocardiograms were classified according to the Minnesota code (25), with tall R waves (codes 3-1 to 3-3) used to reflect left ventricular hypertrophy. Information on smoking and use of antihypertensive drugs and lipid-lowering medication was requested at phases 3, 5, and 7.", "measurement_extractions": [ { "quantity": "at least 5 hours", "unit": "hours", "measured_entity": "Venous blood", "measured_property": "taken" }, { "quantity": "\u22124\u00b0C", "unit": "\u00b0C", "measured_entity": "Serum for lipid analyses", "measured_property": "refrigerated" }, { "quantity": "within 72 hours", "unit": "hours", "measured_entity": "Serum for lipid analyses", "measured_property": "assayed" }, { "quantity": "2 hours", "unit": "hours", "measured_entity": "new venous blood samples", "measured_property": "taken" }, { "quantity": "75 g", "unit": "g", "measured_entity": "glucose solution", "measured_property": null }, { "quantity": "1.4%\u20133.1%", "unit": "%", "measured_entity": "glucose oxidase method", "measured_property": "mean coefficient of variation" }, { "quantity": "\u22657.0 mmol/L", "unit": "mmol/L", "measured_entity": "fasting glucose", "measured_property": null }, { "quantity": "2-hour", "unit": "hour", "measured_entity": "postload glucose", "measured_property": null }, { "quantity": "\u226511.1 mmol/L", "unit": "mmol/L", "measured_entity": "2-hour postload glucose", "measured_property": null }, { "quantity": "5 minutes", "unit": "minutes", "measured_entity": "rest", "measured_property": null } ], "split": "train", "docId": "S0006322312001096-1190", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The validity of the Framingham risk scores as measures of vascular risk was supported in our study, as they strongly predicted incidence of subsequent (manifest) vascular disease. Age- and sex-adjusted odds ratio for 10% increment in risk was 2.84 (95% confidence interval [CI] 1.66\u20134.88) for the stroke risk score-incident stroke association and 2.25 (95% CI 1.92\u20132.65) for the CHD risk score-incident CHD association. The corresponding odds ratios for the associations of CVD risk score with stroke and CHD were 1.34 (95% CI 1.11\u20131.62) and 2.14 (95% CI 1.85\u20132.48), respectively.", "measurement_extractions": [ { "quantity": "10%", "unit": "%", "measured_entity": "risk", "measured_property": "increment" }, { "quantity": "2.84", "unit": null, "measured_entity": "stroke risk score-incident stroke association", "measured_property": "Age- and sex-adjusted odds ratio" }, { "quantity": "95%", "unit": "%", "measured_entity": "confidence interval [CI]", "measured_property": null }, { "quantity": "1.66\u20134.88", "unit": null, "measured_entity": "stroke risk score-incident stroke association", "measured_property": "Age- and sex-adjusted odds ratio" }, { "quantity": "2.25", "unit": null, "measured_entity": "CHD risk score-incident CHD association", "measured_property": "Age- and sex-adjusted odds ratio" }, { "quantity": "95%", "unit": "%", "measured_entity": "CI", "measured_property": null }, { "quantity": "1.92\u20132.65", "unit": null, "measured_entity": "CHD risk score-incident CHD association", "measured_property": "Age- and sex-adjusted odds ratio" }, { "quantity": "1.34", "unit": null, "measured_entity": "CVD", "measured_property": "odds ratios" }, { "quantity": "95%", "unit": "%", "measured_entity": "CI", "measured_property": null }, { "quantity": "1.11\u20131.62", "unit": null, "measured_entity": "CVD", "measured_property": "odds ratios" }, { "quantity": "2.14", "unit": null, "measured_entity": "CHD", "measured_property": "odds ratios" }, { "quantity": "95%", "unit": "%", "measured_entity": "CI", "measured_property": null }, { "quantity": "1.85\u20132.48", "unit": null, "measured_entity": "CHD", "measured_property": "odds ratios" } ], "split": "train", "docId": "S0006322312001096-1194", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We used three indicators (or proxy measures) to identify persons with depressive symptoms. First, participants responded to the self-administered 30-item General Health Questionnaire (GHQ) at phases 3, 5, 7, and 9, a screening instrument designed for and widely used in population-based surveys and trials (26). Each questionnaire item enquires about a specific symptom, with response categories scored as either 1 or 0 to indicate whether the symptom is present. Total score of 5 or more led to individuals being defined as GHQ-symptom cases and scores 0 to 4 as noncases (27). Although the GHQ was originally designed to assess depressive and anxiety symptoms, a recent population-based study showed GHQ caseness to be sensitive (84%) and specific (84%) in detecting dysthymia or major depressive disorder, as indicated by the Composite International Diagnostic Interview (28). The GHQ has also been validated at baseline against a clinical interview schedule in the Whitehall II study, with acceptable sensitivity (73%) and specificity (78%) (27). In a more recent validation using a subgroup of 274 participants aged 58 to 70 in 2010, the sensitivity and specificity of GHQ symptom caseness against diagnosed depressive episodes based on a structured psychiatric interview were 80% and 81%, respectively.", "measurement_extractions": [ { "quantity": "three", "unit": null, "measured_entity": "indicators (or proxy measures)", "measured_property": null }, { "quantity": "30-item", "unit": "item", "measured_entity": "General Health Questionnaire (GHQ)", "measured_property": null }, { "quantity": "1 or 0", "unit": null, "measured_entity": "response categories", "measured_property": "scored" }, { "quantity": "5 or more", "unit": null, "measured_entity": "GHQ-symptom cases", "measured_property": "score" }, { "quantity": "0 to 4", "unit": null, "measured_entity": "noncases", "measured_property": "scores" }, { "quantity": "84%", "unit": "%", "measured_entity": "GHQ caseness", "measured_property": "sensitive" }, { "quantity": "84%", "unit": "%", "measured_entity": "GHQ caseness", "measured_property": "specific" }, { "quantity": "73%", "unit": "%", "measured_entity": "GHQ", "measured_property": "sensitivity" }, { "quantity": "78%", "unit": "%", "measured_entity": "GHQ", "measured_property": "specificity" }, { "quantity": "274 participants", "unit": "participants", "measured_entity": "subgroup", "measured_property": null }, { "quantity": "58 to 70", "unit": null, "measured_entity": "subgroup of 274 participants", "measured_property": "aged" }, { "quantity": "2010", "unit": null, "measured_entity": "more recent validation", "measured_property": null }, { "quantity": "80%", "unit": "%", "measured_entity": "GHQ symptom caseness", "measured_property": "sensitivity" }, { "quantity": "81%", "unit": "%", "measured_entity": "GHQ symptom caseness", "measured_property": "specificity" } ], "split": "train", "docId": "S0006322312001096-1197", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Second, at phases 7 and 9, depressive symptoms were also assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) (note that CES-D was not included in earlier screening phases of our study) (29). The 20 items of the CES-D measure symptoms associated with depression; participants are asked to score the frequency of occurrence of specific symptoms during the previous week on a four point scale (0 = less than one day, 1 = 1\u20132 days, 2 = 3\u20134 days, and 3 = 5\u20137 days). These items are summed to yield a total score between 0 and 60, with participants scoring \u226516 defined as having CES-D depressive symptoms (30). In a validation study of 274 Whitehall II participants, sensitivity and specificity were 89% and 86%, respectively, for CES-D depressive symptoms using a structured psychiatric interview as the criterion.", "measurement_extractions": [ { "quantity": "20 items", "unit": "items", "measured_entity": "CES-D", "measured_property": null }, { "quantity": "four point", "unit": "point", "measured_entity": "scale", "measured_property": null }, { "quantity": "less than one day", "unit": "day", "measured_entity": "four point scale", "measured_property": "0" }, { "quantity": "1\u20132 days", "unit": "days", "measured_entity": "four point scale", "measured_property": "1" }, { "quantity": "3\u20134 days", "unit": "days", "measured_entity": "four point scale", "measured_property": "2" }, { "quantity": "5\u20137 days", "unit": "days", "measured_entity": "four point scale", "measured_property": "3" }, { "quantity": "between 0 and 60", "unit": null, "measured_entity": "total score", "measured_property": null }, { "quantity": "\u226516", "unit": null, "measured_entity": "defined as having CES-D depressive symptoms", "measured_property": "scoring" }, { "quantity": "274 Whitehall II participants", "unit": "Whitehall II participants", "measured_entity": "validation study", "measured_property": null }, { "quantity": "89%", "unit": "%", "measured_entity": "structured psychiatric interview", "measured_property": "sensitivity" }, { "quantity": "86%", "unit": "%", "measured_entity": "structured psychiatric interview", "measured_property": "specificity" } ], "split": "train", "docId": "S0006322312001096-1202", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Third, at phases 3, 5, 7, and 9, participants were asked whether they had taken any medication in the past 14 days and, if so, to provide the name of the medication. Medications were coded using British National Formulary codes to define antidepressant medication use (codes: 040301\u2013040304) (31).", "measurement_extractions": [ { "quantity": "past 14 days", "unit": "days", "measured_entity": "participants", "measured_property": "taken any medication" } ], "split": "train", "docId": "S0006322312001096-1205", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We constructed Framingham CVD, CHD, and stroke risk scores for each participant and used logistic regression to examine the association of each risk score at baseline with depressive symptoms at follow-up. Crude, age- and sex-adjusted, and multivariably adjusted odds ratios and 95% confidence intervals per 10% absolute increase in risk score were calculated. In the analysis of GHQ symptoms, the sample was large enough to detect, with 90% power at a significance level of .05, an odds ratio of 1.14 for a 10% increase in the Framingham CVD risk score. For depressive symptoms defined by CES-D and antidepressant medication, the corresponding odds ratios were 1.40 and 1.24, respectively.", "measurement_extractions": [ { "quantity": "95%", "unit": "%", "measured_entity": "confidence intervals", "measured_property": null }, { "quantity": "10%", "unit": "%", "measured_entity": "risk score", "measured_property": "absolute increase" }, { "quantity": "90%", "unit": "%", "measured_entity": "power", "measured_property": null }, { "quantity": ".05", "unit": null, "measured_entity": "significance level", "measured_property": null }, { "quantity": "1.14", "unit": null, "measured_entity": "10% increase in the Framingham CVD risk score", "measured_property": "odds ratio" }, { "quantity": "10%", "unit": "%", "measured_entity": "Framingham CVD risk score", "measured_property": "increase" }, { "quantity": "1.40", "unit": null, "measured_entity": "depressive symptoms defined by CES-D", "measured_property": "odds ratios" }, { "quantity": "1.24", "unit": null, "measured_entity": "antidepressant medication", "measured_property": "odds ratios" } ], "split": "train", "docId": "S0006322312001096-1221", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Differences between the analytic sample and the excluded participants were generally small (Table S1 in Supplement 1). Compared with those included, participants excluded from the analyses were more likely to be women, nonwhite, and current smokers, although other characteristics were similar in the two groups. Across the 5-year data cycles, 12.1% of participants without GHQ symptoms developed such symptoms, 4.6% of those without CES-D depressive symptoms had such symptoms at follow-up, and 2.1% of those not on antidepressant treatment started such medication. Of the cases of GHQ symptoms, 5.4% used antidepressant medication, the corresponding proportion being 9.2% for those with CES-D depressive symptoms. Of participants with GHQ symptoms, 47.4% also had CES-D depressive symptoms (Pearson correlation at phase 7 r = .64, p < .001). Conversely, among participants with CES-D depressive symptoms, 59.9% also had GHQ symptoms.", "measurement_extractions": [ { "quantity": "5-year", "unit": "year", "measured_entity": "data cycles", "measured_property": null }, { "quantity": "12.1%", "unit": "%", "measured_entity": "participants without GHQ symptoms", "measured_property": "developed such symptoms" }, { "quantity": "4.6%", "unit": "%", "measured_entity": "those without CES-D depressive symptoms", "measured_property": "had such symptoms at follow-up" }, { "quantity": "2.1%", "unit": "%", "measured_entity": "those not on antidepressant treatment", "measured_property": "started such medication" }, { "quantity": "5.4%", "unit": "%", "measured_entity": "cases of GHQ symptoms", "measured_property": "used antidepressant medication" }, { "quantity": "9.2%", "unit": "%", "measured_entity": "those with CES-D depressive symptoms", "measured_property": "used antidepressant medication" }, { "quantity": "47.4%", "unit": "%", "measured_entity": "participants with GHQ symptoms,", "measured_property": "also had CES-D depressive symptoms" }, { "quantity": ".64", "unit": null, "measured_entity": "Pearson correlation at phase 7", "measured_property": "r" }, { "quantity": "< .001", "unit": null, "measured_entity": "Pearson correlation at phase 7", "measured_property": "p" }, { "quantity": "59.9%", "unit": "%", "measured_entity": "participants with CES-D depressive symptoms", "measured_property": "also had GHQ symptoms" } ], "split": "train", "docId": "S0006322312001096-1230", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "After adjustment for multiple covariates (age, sex, marital status, education, socioeconomic status, retirement, cognitive impairment, body mass index, alcohol consumption, menopausal status [women], and nonvascular chronic condition), the association between vascular disease and subsequent CES-D depressive symptoms remained in participants with no prevalent or previous depressive episodes, as indicated by the Composite International Diagnostic Interview (Table 3). Higher stroke risk score was associated with higher odds of onset of CES-D depressive symptoms before (odds ratio 2.30, 95% CI 1.03\u20135.13), but not after, the age 65. The CVD and CHD risk scores were not associated with subsequent depressive symptoms.", "measurement_extractions": [ { "quantity": "95%", "unit": "%", "measured_entity": "CI", "measured_property": null } ], "split": "train", "docId": "S0006322312001096-1248", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "This longitudinal study of British adults shows that manifest cardiovascular disease (CHD and stroke) is associated with increased risk of depressive symptoms. There was also some evidence to suggest an association between higher Framingham stroke risk score and increased onset of depressive symptoms before the age of 65 years. However, the vascular risk scores did not predict later-life depressive symptoms among persons with no manifest vascular disease or history of depression. This finding was based on data from three different vascular risk prediction algorithms\u2014the Framingham CVD, CHD, and stroke scores\u2014and depressive symptoms measured using two survey instruments and information on use of antidepressant medication.", "measurement_extractions": [ { "quantity": "65 years", "unit": "years", "measured_entity": "increased onset of depressive symptoms", "measured_property": "before the age" } ], "split": "train", "docId": "S0006322312001096-1253", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A few previous studies have examined the longitudinal association between overall vascular risk and later-life depression using other indicators of vascular risk status. In the Health, Aging and Body Composition study of adults aged 70 or higher, a score combining selected vascular risk factors (body mass index, metabolic syndrome, smoking, and ankle arm index) and vascular diseases predicted higher 2-year incidence of elevated depressive symptoms (7). However, the extent to which this association was attributable to vascular risk factors alone is difficult to assess in such a study design. In the Rotterdam study of older adults followed up for 6 years (11), none of the objective atherosclerosis measures was linked with subsequent depression. This null finding was evident irrespective of whether assessment of depression was based on CES-D or a clinical diagnosis obtained from a psychiatric interview (11). Similarly, in the Leiden 85+ prospective cohort study, ratings of generalized atherosclerosis were not associated with depressive symptoms (43).", "measurement_extractions": [ { "quantity": "70 or highe", "unit": null, "measured_entity": "adults", "measured_property": "aged" }, { "quantity": "2-year", "unit": "year", "measured_entity": "elevated depressive symptoms", "measured_property": "incidence" }, { "quantity": "for 6 years", "unit": "years", "measured_entity": "older adults", "measured_property": "followed up" }, { "quantity": "none", "unit": null, "measured_entity": "objective atherosclerosis measures", "measured_property": null } ], "split": "train", "docId": "S0006322312001096-1260", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "There are caveats to the results reported here. We measured depressive symptoms using validated instruments, such as the General Health Questionnaire and CES-D, and information on prescribed antidepressant medication use (26,29,49). These instruments are not designed to make a psychiatric diagnosis of first or recurrent major depression (26,29); they defined only partially overlapping case populations and some misclassification occurred because antidepressants are also prescribed for conditions other than depression. Nevertheless, the associations of manifest CHD and stroke with depressive symptoms in our study are in agreement with previous research linking vascular disease to depressive symptoms and clinical depression (4,35\u201338,44). Furthermore, in a validation study of 274 elderly participants from our cohort, sensitivity and specificity of the questionnaire measures with depression diagnosed based on structured psychiatric interview as the criterion are high, almost 90% for CES-D depressive symptoms and approximately 80% for GHQ symptoms.", "measurement_extractions": [ { "quantity": "274 elderly participants", "unit": "elderly participants", "measured_entity": "validation study", "measured_property": null }, { "quantity": "almost 90%", "unit": "%", "measured_entity": "the questionnaire measures", "measured_property": "sensitivity and specificity" }, { "quantity": "approximately 80%", "unit": "%", "measured_entity": "the questionnaire measures", "measured_property": "sensitivity and specificity" } ], "split": "train", "docId": "S0006322312001096-1271", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Despite a high response to the survey (range 66% to 88%) at the successive data collection phases, loss to follow-up accumulated over the extended follow-up, as is inevitable in long-term prospective studies. However, differences between the included and excluded participants were generally small. Our study is based on an occupational cohort, which, by its very nature, is healthier than the general population, so the range of vascular risk scores and the range of depressive symptom measurements are likely to be narrower. This being the case, the associations between the Framingham risk scores and depressive symptoms reported here could underestimate the strength of associations in the general population, although similar associations between manifest vascular disease and depressive symptoms in the present dataset and those from the general population suggest that our estimates are likely to be fairly accurate. Due to the relatively low numbers of depressive symptom cases in the present data, we cannot detect weak associations between vascular risk scores and later-life depressive symptoms.", "measurement_extractions": [ { "quantity": "range 66% to 88%", "unit": "%", "measured_entity": "survey", "measured_property": "response" } ], "split": "train", "docId": "S0006322312001096-1275", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "These results suggest that public health measures to improve vascular status will influence the incidence of later-life depression, primarily via reduced rates of manifest vascular disease. Our findings on diagnosed vascular disease support current clinical guidelines to screen patients with coronary heart disease or stroke for depressive symptoms. However, although the stroke risk score was associated with depressive symptoms before the age of 65, we found little evidence to suggest that standard clinical information on preclinical vascular risk status would be helpful in the prediction of depressive symptoms after the age of 65 years. Thus, extending screening of incident later-life depression to include those identified as having a high risk of developing vascular disease was not supported by this study.", "measurement_extractions": [ { "quantity": "65", "unit": null, "measured_entity": "depressive symptoms", "measured_property": "age" }, { "quantity": "65 years", "unit": "years", "measured_entity": "depressive symptoms", "measured_property": null } ], "split": "train", "docId": "S0006322312001096-1278", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Association Between Framingham Risk Scores (per 10% Increase) and Subsequent Onset of Depressive Symptoms Before and After Age 65", "measurement_extractions": [ { "quantity": "10%", "unit": null, "measured_entity": "Framingham Risk Scores", "measured_property": "Increase" }, { "quantity": "65", "unit": null, "measured_entity": "Depressive Symptoms", "measured_property": "Age" } ], "split": "train", "docId": "S0006322312001096-626", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Environmental changes associated with the Paleocene\u2013Eocene thermal maximum (PETM, \u223c56 Ma) have not yet been documented in detail from the North Sea Basin. Located within proximity to the North Atlantic igneous province (NAIP), the Kilda Basin, and the northern rain belt (paleolatitude 54 \u00b0N) during the PETM, this is a critical region for testing proposed triggers of atmospheric carbon release that may have caused the global negative carbon isotope excursion (CIE) in marine and terrestrial environments. The CIE onset is identified from organic matter \u03b413C in exceptional detail within a highly expanded sedimentary sequence. Pollen and spore assemblages analysed in the same samples for the first time allow a reconstruction of possible changes to vegetation on the surrounding landmass. Multiproxy palynological, geochemical, and sedimentologic records demonstrate enhanced halocline stratification and terrigenous deposition well before (103 yrs) the CIE, interpreted as due to either tectonic uplift possibly from a nearby magmatic intrusion, or increased precipitation and fluvial runoff possibly from an enhanced hydrologic cycle. Stratification and terrigenous deposition increased further at the onset and within the earliest CIE which, coupled with evidence for sea level rise, may be interpreted as resulting from an increase in precipitation over NW Europe consistent with an enhanced hydrologic cycle in response to global warming during the PETM. Palynological evidence indicates a flora dominated by pollen from coastal swamp conifers before the CIE was abruptly replaced with a more diverse assemblage of generalist species including pollen similar to modern alder, fern, and fungal spores. This may have resulted from flooding of coastal areas due to relative sea level rise, and/or ecologic changes forced by climate. A shift towards more diverse angiosperm and pteridophyte vegetation within the early CIE, including pollen similar to modern hickory, documents a long term change to regional vegetation.", "measurement_extractions": [ { "quantity": "\u223c56 Ma", "unit": "Ma", "measured_entity": "Paleocene\u2013Eocene thermal maximum (PETM", "measured_property": null }, { "quantity": "54 \u00b0N", "unit": "\u00b0N", "measured_entity": "North Atlantic igneous province (NAIP), the Kilda Basin, and the northern rain belt", "measured_property": "paleolatitude" }, { "quantity": "before (103 yrs)", "unit": "yrs", "measured_entity": "halocline stratification and terrigenous deposition", "measured_property": null } ], "split": "train", "docId": "S0012821X12004384-1148", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The PETM was a period of geologically-rapid global warming that punctuated a warming Eocene climate 55.8 Ma ago (Charles et al., 2011), and saw sea surface temperatures rise by 5\u20138 \u00b0C from background levels (Zachos et al., 2005; Sluijs et al., 2007). It was associated with a substantial injection of \u03b413C-depleted carbon into the ocean-atmosphere system (see Pagani et al., 2006a) over <20 ka (Cui et al., 2011), causing a negative carbon isotope excursion (CIE) between \u22122 and \u22127\u2030 in marine and terrestrial sediments (see overview in Schouten et al., 2007) lasting 170 ka (R\u00f6hl et al., 2007), and a prominent dissolution horizon in the deep sea signifying deep ocean acidification (Kennett and Stott 1991; Zachos et al., 2005). The source and rate of released carbon are still under debate (Pagani et al., 2006a; Zeebe et al., 2009; Cui et al., 2011), but may have been linked to the dissociation of marine hydrates containing biogenic methane (\u03b413C of<\u221260\u2030) (Dickens et al., 1995), thermogenic methane from marine sediments around the Norwegian Sea (Svensen et al., 2004), or dissolved methane from a silled Kilda Basin between Greenland and Norway (Nisbet et al., 2009).", "measurement_extractions": [ { "quantity": "55.8 Ma", "unit": "Ma", "measured_entity": "PETM", "measured_property": null }, { "quantity": "5\u20138 \u00b0C", "unit": "\u00b0C", "measured_entity": "sea surface temperatures", "measured_property": "rise" }, { "quantity": "over <20 ka", "unit": "ka", "measured_entity": "\u03b413C-depleted carbon", "measured_property": "substantial injection" }, { "quantity": "between \u22122 and \u22127\u2030", "unit": "\u2030", "measured_entity": "marine and terrestrial sediments", "measured_property": "negative carbon isotope" }, { "quantity": "170 ka", "unit": "ka", "measured_entity": "negative carbon isotope excursion (CIE)", "measured_property": "lasting" }, { "quantity": "<\u221260\u2030", "unit": "\u2030", "measured_entity": "marine hydrates containing biogenic methane", "measured_property": "\u03b413C" } ], "split": "train", "docId": "S0012821X12004384-1178", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "During the late Paleocene\u2013early Eocene the North Sea was a restricted marine basin, characterised by siliciclastic sedimentation and high terrigenous input, principally from the Scotland\u2013Faeroe\u2013Shetland landmass (Knox 1998, Fig. S1). Core 22/10a-4 is located in the central part of the basin (Figs. 1 and S1) and is therefore disconnected from many marginal marine processes that could mask oceanographic signals (e.g. tidal or storm-induced erosion and slumping). Paleobathymetry estimates in the North Sea during the Paleocene and Eocene are difficult to constrain accurately, as the extant benthic foraminifera present in the Paleogene are found today living between 200 and >1000 m water depth (Gradstein et al., 1992), and are controlled predominantly by substrate and bottom water properties. However using a number of paleoecologic micropaleontology methods together (Gillmore et al., 2001), along with 2D structural restoration (Kjennerud and Sylta, 2001), broad agreement was found and central parts of the northern North Sea appear to have had paleodepths of >0.5 km in the earliest Eocene near 22/10a-4 (Kjennerud and Gillmore, 2003, Fig. S1).", "measurement_extractions": [ { "quantity": "between 200 and >1000 m", "unit": "m", "measured_entity": "water", "measured_property": "depth" }, { "quantity": ">0.5 km", "unit": "km", "measured_entity": "central parts of the northern North Sea", "measured_property": "paleodepths" } ], "split": "train", "docId": "S0012821X12004384-1221", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "As 22/10a-4 is in the deep (>0.5 km) central part of the basin, it acted as a depocentre and exhibits a Paleocene-Eocene transition sequence that is not only expanded but is also close to being stratigraphically complete. The only evidence for breaks in the succession is minor erosion at the base of thin turbidite sandstones (typically <10 cm). Because these sandstones may contain reworked material, they were not sampled in this study. During the late Paleocene, the basin became restricted following a fall in in the order of 100 m that resulted from regional uplift associated with the proto-Iceland mantle plume in the North Atlantic (see Knox, 1996). This event is evident in 22/10a-4 as a lithologic change from unbedded to bedded mudstone (the Lista and Sele Formation boundary, Fig. 2). Restriction of the basin also led to the establishment of poorly oxygenated bottom waters, as is evident by a shift in the benthic foraminiferal assemblages towards low diversity low oxygen-tolerant agglutinated species (Knox, 1996). The CIE at the Paleocene\u2013Eocene boundary was accompanied by a relative sea level rise, as documented in southeast England (Powell et al., 1996) and Spitsbergen (Harding et al., 2011), due to the thermal expansion of sea water and possible melting of ice caps, although the North Sea basin remained restricted as evidenced by the persistence of low oxygen facies in 22/10a-4. The North Sea had a widespread freshwater catchment area, and a halocline was in place from the late Paleocene to early Eocene (Zacke et al., 2009). Therefore, surface water salinity changes in the North Sea Basin provide a sensitive gauge for stratification forced by changes in tectonics and the hydrologic cycle.", "measurement_extractions": [ { "quantity": ">0.5 km)", "unit": "km", "measured_entity": "central part of the basin", "measured_property": "deep" }, { "quantity": "<10 cm", "unit": "cm", "measured_entity": "turbidite sandstones", "measured_property": "thin" }, { "quantity": "in the order of 100 m", "unit": "m", "measured_entity": "basin", "measured_property": "fall in" } ], "split": "train", "docId": "S0012821X12004384-1232", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Borehole 22/10a-4 (57\u00b044\u20198.47\u201dN; 1\u00b050\u201926.59\u201dE) provides a continuous core through the Forties Sandstone Member and into the Lista Formation (Fig. 2). The core consists of variably fissile claystone with interbedded fine to coarse grained sandstone layers interpreted as turbidites, with occasional mm-thick ash layers (Fig. 3). All samples in this study were taken from claystone horizons to avoid sampling substantial quantities of reworked material. The section of 22/10a-4 analysed in this study is from 2605 m to 2634 m (core depth), chosen because this part of the core is predominantly in claystone facies and provides a greatly expanded section over the onset of the CIE (Figs. 2 and 3). At 2609\u20132613 m the claystone becomes finely laminated with alternately pale and dark laminae couplets ranging from 1 to 25 per mm (Fig. S2). The pale laminae consist of clay and silt, and the dark laminae are rich in organic carbon and pyrite inclusions. Laminae were counted at 26 horizons throughout the core and approximately 13 pairs per mm.", "measurement_extractions": [ { "quantity": "57\u00b044\u20198.47\u201dN; 1\u00b050\u201926.59\u201dE", "unit": null, "measured_entity": "Borehole 22/10a-4", "measured_property": null }, { "quantity": "from 2605 m to 2634 m", "unit": "m", "measured_entity": "section of 22/10a-4", "measured_property": "core depth" }, { "quantity": "2609\u20132613 m", "unit": "m", "measured_entity": "the claystone", "measured_property": "becomes finely laminated" }, { "quantity": "ranging from 1 to 25 per mm", "unit": "mm", "measured_entity": "alternately pale and dark laminae couplets", "measured_property": null }, { "quantity": "26 horizons", "unit": "horizons", "measured_entity": "core", "measured_property": null }, { "quantity": "approximately 13 pairs per mm", "unit": "pairs per mm", "measured_entity": "Laminae", "measured_property": null } ], "split": "train", "docId": "S0012821X12004384-1249", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A total of 71 palynology samples were prepared at the British Geological Survey using standard preparation procedures (Moore et al., 1991). Samples were demineralised with hydrochloric (HCl) and hydrofluoric (HF) acids, and residual mineral grains removed using heavy liquid (zinc bromide) separation. Elvacite was used to mount slides. The palynomorphs were analysed using a Nikon transmitted light microscope, counting the total number of palynomorphs on a strew slide (Table S1). Each slide was produced from 1/100th of the total material processed, where the initial weight of material was 5 g of dried sediment. Thus, the palynology counts represent the total number of specimens per 0.05 g of dried sediment. Statistical analysis was carried out using the software of Hammer et al. (2005). The %wood/plant tissue was determined by palynological investigation, and is the sum of \u2018%wood plant tissue\u2019 and \u2018%various (non-woody) plant tissue\u2019 in Table S1. Organic material for \u03b413CAOM analysis was collected from the same palynology samples, and the remaining processed material separated into size fractions. The >250 \u03bcm fractions, found through light microscope analysis to be dominated (>90%) by amorphous organic matter (AOM), were also analysed for \u03b413C. Foraminifera samples between 20 and 60 g of dried sediment were processed by washing through a 63 \u03bcm sieve with water. All specimens were counted and converted to foraminifera/g (Table S2). All species exhibited agglutinated (non calcareous) test walls.", "measurement_extractions": [ { "quantity": "71", "unit": null, "measured_entity": "palynology samples", "measured_property": null }, { "quantity": "1/100th", "unit": null, "measured_entity": "total material processed", "measured_property": "Each slide was produced" }, { "quantity": "5 g", "unit": "g", "measured_entity": "dried sediment", "measured_property": "initial weight" }, { "quantity": "0.05 g", "unit": "g", "measured_entity": "dried sediment", "measured_property": null }, { "quantity": ">250 \u03bcm", "unit": "\u03bcm", "measured_entity": null, "measured_property": "fractions" }, { "quantity": ">90%", "unit": "%", "measured_entity": ">250 \u03bcm fractions", "measured_property": "amorphous organic matter (AOM)" }, { "quantity": "between 20 and 60 g", "unit": "g", "measured_entity": "dried sediment", "measured_property": null }, { "quantity": "63 \u03bcm", "unit": "\u03bcm", "measured_entity": "sieve", "measured_property": null } ], "split": "train", "docId": "S0012821X12004384-1265", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "All analyses were carried out at the NERC Isotope Geosciences Laboratory. C and N analyses (from which we present weight % C/N) were performed on 225 samples by combustion in a Costech ECS4010 Elemental Analyser (EA) calibrated against an acetanilide standard (Table S3). C/N atomic ratios were calculated by multiplying by 1.167. Replicate analysis of well-mixed samples indicated a precision of \u00b1<0.1. Carbon isotope analysis was carried out on 289 bulk rock samples (Table S4) after removing migrated hydrocarbons (Stephenson et al., 2005). The hydrocarbons were removed by crushing the rock fragments using a ball mill, and the soluble organic matter from all rock samples was extracted using a Soxhlet extractor. The samples were refluxed for 24 h in an azeotropic mixture of dichloromethane and methanol (93:7, v/v). All materials (cellulose Soxhlet thimbles, silica wool, vials) were cleaned with analytical grade organic solvents prior to use. Any remaining solvent was then removed by evaporation and the dried sediments were transferred to vials. Any calcites (shelly fragments) were removed by placing the samples in 5% HCl overnight before rinsing and drying down. Carbon isotope analysis was also carried out on palynology residues of the >250 \u03bcm size fractions dominated by AOM. 13C/12C analyses were performed on 35 samples by combustion in a Costech Elemental Analyser (EA) online to a VG TripleTrap and Optima dual-inlet mass spectrometer, with \u03b413C values calculated to the VPDB scale using a within-run laboratory standards calibrated against NBS-18, NBS-19, and NBS-22. Replicate 13C/12C analyses were carried out on the section, and the mean standard deviation on the replicate analyses is 0.4\u2030.", "measurement_extractions": [ { "quantity": "225", "unit": null, "measured_entity": "samples", "measured_property": null }, { "quantity": "1.167", "unit": null, "measured_entity": null, "measured_property": null }, { "quantity": "\u00b1<0.1", "unit": null, "measured_entity": "Replicate analysis", "measured_property": "precision" }, { "quantity": "289", "unit": null, "measured_entity": "bulk rock samples", "measured_property": null }, { "quantity": "24 h", "unit": "h", "measured_entity": "samples", "measured_property": "were refluxed for" }, { "quantity": "93:7, v/v", "unit": "v/v", "measured_entity": "azeotropic mixture of dichloromethane and methanol", "measured_property": null }, { "quantity": "5%", "unit": "%", "measured_entity": "HCl", "measured_property": null }, { "quantity": ">250 \u03bcm", "unit": "\u03bcm", "measured_entity": null, "measured_property": "size fractions" }, { "quantity": "35", "unit": null, "measured_entity": "samples", "measured_property": null }, { "quantity": "0.4\u2030", "unit": "\u2030", "measured_entity": "replicate analyses", "measured_property": "mean standard deviation" } ], "split": "train", "docId": "S0012821X12004384-1284", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A negative carbon isotope excursion of 5\u2030 has been identified from \u03b413CTOC and \u03b413CAOM in an expanded Paleocene\u2013Eocene boundary section from the central North Sea Basin. Palynological (dinoflagellate cyst, pollen, and spore assemblages) and sedimentologic (C/N ratios and kaolinite distribution) evidence indicate major changes occurred to marine and terrestrial environments in NW Europe both preceding and over the CIE. Enhanced halocline stratification and terrigenous input from 4 m before the CIE may indicate tectonic uplift and oceanic restriction of the North Sea, supporting hypotheses for NAIP volcanism as a trigger for the CIE (Svensen et al., 2004), and/or increased terrigenous runoff and regional precipitation, supporting hypotheses of an enhanced hydrologic cycle triggering carbon release (Bice and Marotzke, 2002). A peak in Apectodinium before the CIE is interpreted as an ephemeral increase in terrestrial runoff causing local eutrophication. Further enhanced halocline stratification and terrigenous input at and immediately after the CIE onset, coupled with evidence for sea level rise in coastal areas, indicate possible increased regional precipitation over NW Europe. At this location (paleolatitude 54 oN) increased precipitation would support the hypothesis that a poleward migration of storm tracks from an enhanced hydrologic cycle resulted from global warming during the PETM (Pagani et al., 2006b).", "measurement_extractions": [ { "quantity": "5\u2030", "unit": "\u2030", "measured_entity": "Paleocene\u2013Eocene boundary section", "measured_property": "negative carbon isotope excursion" }, { "quantity": "from 4 m", "unit": "m", "measured_entity": "Enhanced halocline stratification and terrigenous input", "measured_property": null }, { "quantity": "54 oN", "unit": "oN", "measured_entity": "location", "measured_property": "paleolatitude" } ], "split": "train", "docId": "S0012821X12004384-1640", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Carbon isotopic results of total organic matter (\u03b413CTOC) and amorphous organic matter (\u03b413CAOM) against core 22/10a-4 lithology and Apectodinium spp. (%). Blue=bulk rock \u03b413CTOC; black=\u03b413CAOM; solid red symbols=bulk rock \u03b413CTOC from samples with <30% wood/plant tissue (determined from palynological residue of the sample); open red symbols=bulk rock \u03b413CTOC from samples with >30% wood/plant tissue. The first appearance of Apectodinium augustum identifies the PETM in the North Sea (Bujak and Brinkhuis, 1998), and the first negative shift in \u03b413C identifies the approximate position of the CIE onset and the Paleocene\u2013Eocene boundary. Values shaded at 2614.7 and 2619.6 m are considered possible outliers based on statistical analysis of the palynological residues (see Section 4.1). Lithologic column shows position of sand intervals (yellow), claystone intervals (brown; predominantly laminated claystone, dark brown), and ash layers (pink). (For interpretation of the references to color in this figure caption, the reader is referred to the web version of this article.)", "measurement_extractions": [ { "quantity": "<30%", "unit": "%", "measured_entity": "samples", "measured_property": "wood/plant tissue" }, { "quantity": ">30%", "unit": "%", "measured_entity": "samples", "measured_property": "wood/plant tissue" }, { "quantity": "2614.7 and 2619.6 m", "unit": "m", "measured_entity": "Values shaded", "measured_property": null } ], "split": "train", "docId": "S0012821X12004384-952", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The abrupt onset of Antarctic glaciation during the Eocene\u2013Oligocene Transition (\u223c33.7 Ma, Oi1) is linked to declining atmospheric pCO2 levels, yet the mechanisms that forced pCO2 decline remain elusive. Biogenic silicon cycling is inextricably linked to both long and short term carbon cycling through the diatoms, siliceous walled autotrophs which today account for up to 40% of primary production. It is hypothesised that during the Late Eocene a sharp rise in diatom abundance could have contributed to pCO2 drawdown and global cooling by increasing the proportion of organic carbon buried in marine sediment. Diatom and sponge silicon isotope ratios (\u03b430Si) are here combined for the first time to reconstruct the late Eocene\u2013early Oligocene ocean silicon cycle and provide new insight into the role of diatom productivity in Antarctic glaciation. At ODP site 1090 in the Southern Ocean, a 0.6\u2030 rise in diatom \u03b430Si through the late Eocene documents increasing diatom silicic acid utilisation with high, near modern values attained by the earliest Oligocene. A concomitant 1.5\u2030 decline in sponge \u03b430Si at ODP site 689 on the Maud Rise tracks an approximate doubling of intermediate depth silicic acid concentration in the high southern latitudes. Intermediate depth silicic acid concentration peaked at \u223c31.5 Ma, coincident with the final establishment of a deepwater pathway through the Tasman Gateway and Drake Passage. These results suggest that upwelling intensification related to the spin-up of a circum-Antarctic current may have driven late Eocene diatom proliferation. Organic carbon burial associated with higher diatom abundance and export provides a mechanism that can account for pCO2 drawdown not only at, but also prior to, Antarctic glaciation as required by a pCO2 \u2018threshold\u2019 mechanism for ice sheet growth.", "measurement_extractions": [ { "quantity": "\u223c33.7 Ma", "unit": "Ma", "measured_entity": "Oi1", "measured_property": null }, { "quantity": "up to 40%", "unit": "%", "measured_entity": "primary production", "measured_property": "siliceous walled autotrophs" }, { "quantity": "0.6\u2030", "unit": "\u2030", "measured_entity": "diatom \u03b430Si", "measured_property": "rise" }, { "quantity": "1.5\u2030", "unit": "\u2030", "measured_entity": "sponge \u03b430Si", "measured_property": "decline" }, { "quantity": "\u223c31.5 Ma", "unit": "Ma", "measured_entity": "Intermediate depth silicic acid concentration", "measured_property": "peaked" } ], "split": "train", "docId": "S0012821X13002185-994", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In the Furlo section the CTBI lies within the Scaglia Bianca Formation, which includes abundant biosiliceous limestone. The Livello Bonarelli is a 1 m thick condensed interval of millimetre-laminated black shale and brown radiolarian sand that represents the sedimentary expression of part of OAE 2 (Arthur and Premoli Silva, 1982). Up to 20 m beneath the Bonarelli level there are numerous centimetre scale organic-rich shale layers (Jenkyns et al., 2007). The \u03b413Corg record has a narrow variation in background values prior to OAE 2, \u223c\u221225.9 to \u221226.5\u2030. The characteristic positive excursion in \u03b413Corg is a 4\u2030 shift, \u221227.2 to \u221223.1\u2030, occurring within <0.5 m (Fig. 2; Supplementary Material, Table 1e).", "measurement_extractions": [ { "quantity": "1 m", "unit": "m", "measured_entity": "Livello Bonarelli", "measured_property": "thick" }, { "quantity": "Up to 20 m", "unit": "m", "measured_entity": "numerous centimetre scale organic-rich shale layers", "measured_property": "beneath the Bonarelli" }, { "quantity": "\u223c\u221225.9 to \u221226.5\u2030.", "unit": "\u2030", "measured_entity": "\u03b413Corg record", "measured_property": "narrow variation in background values prior to OAE 2" }, { "quantity": "4\u2030", "unit": "\u2030", "measured_entity": "characteristic positive excursion in \u03b413Corg", "measured_property": "shift" }, { "quantity": "\u221227.2 to \u221223.1\u2030", "unit": "\u2030", "measured_entity": "characteristic positive excursion in \u03b413Corg", "measured_property": "shift" }, { "quantity": "within <0.5 m", "unit": "m", "measured_entity": "characteristic positive excursion in \u03b413Corg", "measured_property": "shift" } ], "split": "train", "docId": "S0012821X13007309-1691", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Sensitivity analysis carried out on MTDATA software, for 9 and 21 trace elements.", "measurement_extractions": [ { "quantity": "9 and 21", "unit": null, "measured_entity": "trace elements", "measured_property": null } ], "split": "train", "docId": "S0016236113008041-2924", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The fate of trace elements was investigated in a 90 kW oxy-combustion pilot plant fed with coal and limestone [7]. It was shown that 82% of elemental Hg was emitted in the exhaust gas, as was 81% of Cl. It was further suggested that the relatively low temperatures, and high Ca content in the system from limestone use promoted condensation and sorption of sulphate, fluoride and chloride species.", "measurement_extractions": [ { "quantity": "90 kW", "unit": "kW", "measured_entity": "oxy-combustion pilot plant", "measured_property": null }, { "quantity": "82%", "unit": "%", "measured_entity": "elemental Hg", "measured_property": "elemental Hg was emitted in the exhaust gas" }, { "quantity": "81%", "unit": "%", "measured_entity": "Cl", "measured_property": null } ], "split": "train", "docId": "S0016236113008041-3012", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Those elements at the higher concentrations in the flue gas include Na, Si, K, Zn, and Br. For Na in particular which was present in reduced amounts in the solid, this suggests that some of it may be partitioning to the flue gas for bed inventories 6 and 13 kg.", "measurement_extractions": [ { "quantity": "6 and 13 kg", "unit": "kg", "measured_entity": "bed inventories", "measured_property": null } ], "split": "train", "docId": "S0016236113008041-3159", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For Na, Al, K and Fe, the lowest bed inventory of 4.5 kg resulted in a concentration in the flue gas which was less than that of the blank sample, values which then increased for the higher bed inventories. This suggests that a certain amount of these elements is being absorbed, perhaps by the sorbent in the case of Fe, or by the reactor itself. In the case of P, decreased values compared to the blank were found for bed inventories of 4.5 kg and 13 kg, but an increased value for 6 kg inventory. However, the values are very low at <0.01 ppm and this anomalous result may be due to analytical errors. Overall, although concentrations in the flue gas are small at <2 ppm, increasing bed inventory does appear to increase the concentration of the majority of major elements present in the flue gas.", "measurement_extractions": [ { "quantity": "4.5 kg", "unit": "kg", "measured_entity": "lowest bed inventory", "measured_property": null }, { "quantity": "4.5 kg and 13 kg", "unit": "kg", "measured_entity": "bed inventories", "measured_property": null }, { "quantity": "6 kg", "unit": "kg", "measured_entity": "inventory", "measured_property": null }, { "quantity": "<0.01 ppm", "unit": "ppm", "measured_entity": "P", "measured_property": "values" }, { "quantity": "<2 ppm", "unit": "ppm", "measured_entity": "flue gas", "measured_property": "concentrations" } ], "split": "train", "docId": "S0016236113008041-3161", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Effect of bed inventory on increase of solid major elemental concentrations for bed inventories of 4.5 kg, 6 kg and 13 kg CaCO3.", "measurement_extractions": [ { "quantity": "4.5 kg, 6 kg and 13 kg", "unit": "kg", "measured_entity": "bed inventories", "measured_property": null } ], "split": "train", "docId": "S0016236113008041-872", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "SEM\u2013EDS analysis of sorbent for reactions undertaken with different SO2 concentrations, showing error bars of +1%.", "measurement_extractions": [ { "quantity": "+1%", "unit": "%", "measured_entity": "error bars", "measured_property": null } ], "split": "train", "docId": "S0016236113008041-961", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Enceladus, one out of currently 62 satellites of Saturn, orbits the planet at a distance of 3.95 Saturn radii RS (1RS = 60,268 km) and is embedded in the radiation belts of Saturn\u2019s inner magnetosphere. Even with a small radius REnc of only 252 km and an apparent similarity to other icy moons, this moon is by far the most important internal source of dust, neutral gas and plasma in the saturnian system and especially in the magnetosphere. As already inferred from data of the first flybys of the Pioneer 11 (1979), Voyager 1 (1980) and Voyager 2 (1981) spacecraft, respectively (Smith et al., 1981; Krimigis et al., 1982), this moon plays the same role Io does for the jovian system. The Cassini spacecraft, in orbit around Saturn since July 2004, has flown by Enceladus 14 times between 2005 and 2010. Data from Cassini instrument teams identified a plume of water geysers above the so called \u201ctigerstripes\u201d on the south pole of the moon (Dougherty et al., 2006; Porco et al., 2006). This discovery is one of the most important findings to better understand the saturnian magnetosphere. The material released in the form of water gas, ice molecules and dust (Waite et al., 2006; Spahn et al., 2006) from those geysers is the main source of the E-ring and the neutral torus and is the major plasma source of water group ions in the magnetosphere. A summary of Enceladus findings from Cassini measurements is very well presented by Spencer et al. (2009).", "measurement_extractions": [ { "quantity": "62 satellites", "unit": null, "measured_entity": "Saturn", "measured_property": null }, { "quantity": "3.95 Saturn radii RS", "unit": "Saturn radii RS", "measured_entity": "Enceladus", "measured_property": "distance" }, { "quantity": "60,268 km", "unit": "km", "measured_entity": "1RS", "measured_property": null }, { "quantity": "252 km", "unit": "km", "measured_entity": "Enceladus", "measured_property": "radius REnc" }, { "quantity": "1979", "unit": null, "measured_entity": "Pioneer 11", "measured_property": "first flybys" }, { "quantity": "1980", "unit": null, "measured_entity": "Voyager 1", "measured_property": "first flybys" }, { "quantity": "1981", "unit": null, "measured_entity": "Voyager 2", "measured_property": "first flybys" }, { "quantity": "since July 2004", "unit": null, "measured_entity": "Cassini spacecraft", "measured_property": "orbit around Saturn" }, { "quantity": "14 times", "unit": null, "measured_entity": "Cassini spacecraft", "measured_property": "flown by Enceladus" }, { "quantity": "between 2005 and 2010", "unit": null, "measured_entity": "Cassini spacecraft", "measured_property": "flown by Enceladus 14 times" } ], "split": "train", "docId": "S0019103511004994-1399", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Illustrated electron drift paths for different energies near Enceladus in a perturbed electric field configuration. Only the equatorial plane is shown where x points in the flow direction and y points toward the planet. The energy is mentioned on the top of each figure, The parameter a = 0.1 is the assumed surface velocity of 10% relative to the upstream velocity.", "measurement_extractions": [ { "quantity": "0.1", "unit": null, "measured_entity": "parameter a", "measured_property": null }, { "quantity": "10%", "unit": "%", "measured_entity": "upstream velocity", "measured_property": "assumed surface velocity" } ], "split": "train", "docId": "S0019103511004994-996", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "H-band slit-viewing VLT images taken on 6 November 2011 UT, just two days before the close approach of BS1 and BS2. (The relatively poor quality of the images arises from optimization of image quality for the science detector, which records spectra, instead of the slit viewing detector. When these images were taken, both detectors could not be in good focus at the same time.) It appears that only BS1 was recorded in this image sequence, perhaps because the filter penetration depth is too shallow to see the apparently deeper BS2 feature, which should have crossed the central meridian about 30 min after BS1. It may also be that BS2 was of lower reflectivity before passing BS1 than it was afterwards (it is quite obvious in the K\u2032 image of Fig. 13).", "measurement_extractions": [ { "quantity": "6 November 2011 UT", "unit": "UT", "measured_entity": "H-band slit-viewing VLT images", "measured_property": "taken" }, { "quantity": "two days", "unit": "days", "measured_entity": "H-band slit-viewing VLT images", "measured_property": "taken" }, { "quantity": "30 min", "unit": "min", "measured_entity": "deeper BS2 feature", "measured_property": "should have crossed the central meridian" } ], "split": "train", "docId": "S0019103512001388-1070", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The observed peak in the fractional integrated differential brightness of BS1 in the H filter was observed to be 0.64% in the discovery image on 26 October 2011, and declined to 0.02% by December 16.", "measurement_extractions": [ { "quantity": "0.64%", "unit": "%", "measured_entity": "H filter", "measured_property": "fractional integrated differential brightness of BS1" }, { "quantity": "26 October 2011", "unit": null, "measured_entity": "discovery image", "measured_property": null }, { "quantity": "0.02%", "unit": "%", "measured_entity": "H filter", "measured_property": "fractional integrated differential brightness of BS1" }, { "quantity": "December 16", "unit": null, "measured_entity": "differential brightness of BS1 in the H filter", "measured_property": "declined" } ], "split": "train", "docId": "S0019103512001388-3081", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Notice also that background \u2223B\u2223 is similar to what is measured at all flybys except R1. During the latter, \u2223B\u2223 was on average 3\u20134 nT stronger compared to all other flybys.", "measurement_extractions": [ { "quantity": "average 3\u20134 nT", "unit": "nT", "measured_entity": "\u2223B\u2223", "measured_property": "stronger" } ], "split": "train", "docId": "S0019103512002801-1716", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Observed deviations from the typical, plasma-absorbing interaction region profile may explain some differences. These deviations do not necessarily mean the main interaction mode at Rhea is not plasma absorption. For instance, Simon et al. (2012) demonstrates that the combination of low magnetosonic Mach number and the high plasma beta values of Rhea\u2019s space environment amplifies interaction features which for other plasma absorbers (e.g. Tethys, Dione, Earth\u2019s Moon) are barely detectable. One of these features is a stronger, flow-aligned magnetic field component perturbation, which gives rise to mass-loading-like interaction signatures. Such structures may also complicate electron drifts, but to what extent it is uncertain. Similarly, the high plasma beta makes surface charging more important for Rhea compared to the other Saturnian moons (Roussos et al., 2010). Furthermore, recent developments in the study of Earth\u2019s Moon interaction with the solar wind indicate that the standard picture for a lunar-type interaction may be too simplified: processes, such as the entry of exospheric pick-up ions in the center of the wake, backscattering on the surface and self-pick up of ambient plasma ions, appear to also have an impact on the wake dynamics (Halekas et al., 2011). For instance, the self-pick up process has been shown to lead to enhanced ULF wave activity in the lunar, at least 10% of the time (Nakagawa et al., 2012). Whether a similar process is important the inferred instability at Rhea is questionable, as the latter appears to operate continuously.", "measurement_extractions": [ { "quantity": "One", "unit": null, "measured_entity": "features", "measured_property": null }, { "quantity": "at least 10%", "unit": "%", "measured_entity": "enhanced ULF wave activity in the lunar", "measured_property": "self-pick up process has been shown to lead to" } ], "split": "train", "docId": "S0019103512002801-2018", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A new addition to the thermospheric energy equation is the inclusion of H3+ cooling, a process known to be important on Jupiter (Miller et al., 2006, 2010). At thermospheric temperatures typically found on Saturn (320\u2013500 K, Nagy et al., 2009), we do not expect H3+ cooling to play an important role, but we included the process to be able to assess its importance for cases where polar magnetospheric heating raises temperatures above \u223c500 K. We implemented globally the H3+ cooling rates of Miller et al. (2010) in the form of a parameterisation as a function of local thermospheric temperature and H3+ density.", "measurement_extractions": [ { "quantity": "320\u2013500 K", "unit": "K", "measured_entity": "Saturn", "measured_property": "thermospheric temperatures" }, { "quantity": "above \u223c500 K", "unit": "K", "measured_entity": "Saturn", "measured_property": "temperatures" } ], "split": "train", "docId": "S0019103512003533-3306", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Density profiles O and H based on the C2 model (Paper I) and the density profile of H based on the empirical model of K10 with a mean temperature of 7200 K.", "measurement_extractions": [ { "quantity": "7200 K", "unit": "K", "measured_entity": "empirical model of K10", "measured_property": "mean temperature" } ], "split": "train", "docId": "S0019103512003995-1283", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Moutou et al. (2001) looked for absorption by species such as Na,H,He,CH+,CO+,N2+, and H2O+ in the upper atmosphere of HD209458b. These observations were followed by Moutou et al. (2003) who attempted to measure the transit depth in the He 1083 nm line that was predicted to be significant by Seager and Sasselov (2000). The most recent searches were reported by Winn et al. (2004) and Narita et al. (2005) who looked for transits in the Na D, Li, H\u03b1, H\u03b2, H\u03b3, Fe, and Ca absorption lines. So far none of the ground-based searches have led to a detection of the upper atmosphere. However, the non-detection is based on only a few observations that have proven difficult to analyze, and the search should continue.", "measurement_extractions": [ { "quantity": "1083 nm", "unit": "nm", "measured_entity": "He", "measured_property": "line" } ], "split": "train", "docId": "S0019103512003995-1767", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The cutoff level of the empirical model is somewhat arbitrary. For neutral species it is partly based on ionization (K10), but this criterion obviously does not apply to ions. With a cutoff level at 5Rp for the ions only, the M7 model yields line-integrated transit depths of 3.9%, 8%, and 5.8% in the C II 1334.5 \u00c5, C II 1335.7 \u00c5, and Si III lines, respectively, if 40% of silicon is Si2+. These values agree with the observed values to better than 2\u03c3. Similarly, by extending the cutoff level of the C2 model to 5Rp, we obtained transit depths of 3.2%, 6.7%, and 4.6% in the C II 1334.5 \u00c5, C II 1335.7 \u00c5, and Si III lines, respectively (see Table 2). These values deviate from the observed values by 2\u03c3, 0.9\u03c3, and 2.6\u03c3, respectively. The transit depths predicted by the M7 model are higher partly because the mean temperature of 8250 K is higher than the corresponding temperature in the C2 model (Paper I). This also leads to the higher Si2+/Si+ ratio that we used here.", "measurement_extractions": [ { "quantity": "5Rp", "unit": "Rp", "measured_entity": "M7 model", "measured_property": "cutoff level" }, { "quantity": "3.9%", "unit": "%", "measured_entity": "M7 model", "measured_property": "line-integrated transit depths" }, { "quantity": "8%", "unit": "%", "measured_entity": "M7 model", "measured_property": "line-integrated transit depths" }, { "quantity": "5.8%", "unit": "%", "measured_entity": "M7 model", "measured_property": "line-integrated transit depths" }, { "quantity": "40%", "unit": "%", "measured_entity": "silicon", "measured_property": "Si2+" }, { "quantity": "5Rp", "unit": "Rp", "measured_entity": "C2 model", "measured_property": "cutoff level" }, { "quantity": "3.2%", "unit": "%", "measured_entity": "C2 model", "measured_property": "transit depths" }, { "quantity": "6.7%", "unit": "%", "measured_entity": "C2 model", "measured_property": "transit depths" }, { "quantity": "4.6%", "unit": "%", "measured_entity": "C2 model", "measured_property": "transit depths" }, { "quantity": "8250 K", "unit": "K", "measured_entity": "M7 model", "measured_property": "mean temperature" } ], "split": "train", "docId": "S0019103512003995-2681", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In Paper I we noted that the stellar XUV flux, or the corresponding alternative heat source, would have to be 5\u201310 stronger than the average solar flux to produce a mean temperature between 8000 and 9000 K. Under such circumstances, the predicted transit depths in the C II and Si III lines would obviously be even higher than the values predicted by the SOL2 model. Indeed, higher temperatures broaden absorption in the wings of the line profiles and may help to explain the in-transit flux differences better (see Fig. 7). However, the energy input and temperature in the model cannot be increased without bound. Higher temperatures and flux lead to more efficient ionization of the neutral species, and as a result the transit depths in the H Lyman \u03b1 and O I lines begin to decrease. Also, mass loss rates of 109\u20131010 kg s\u22121 lead to the loss of 10\u2013100% of the planet\u2019s mass over the estimated lifetime of the system, and this probably limits reasonable energy inputs to less than \u223c10 times the solar average on HD209458b.", "measurement_extractions": [ { "quantity": "5\u201310", "unit": null, "measured_entity": "stellar XUV flux, or the corresponding alternative heat source", "measured_property": "stronger" }, { "quantity": "between 8000 and 9000 K", "unit": "K", "measured_entity": "mean temperature", "measured_property": null }, { "quantity": "109\u20131010 kg s\u22121", "unit": "kg s\u22121", "measured_entity": "mass", "measured_property": "loss rates" }, { "quantity": "10\u2013100%", "unit": "%", "measured_entity": "planet\u2019s mass", "measured_property": "loss" }, { "quantity": "less than \u223c10 times", "unit": "times", "measured_entity": "solar average on HD209458b", "measured_property": "energy inputs" } ], "split": "train", "docId": "S0019103512003995-2737", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The detections of atomic hydrogen, heavy atoms and ions surrounding the extrasolar giant planet (EGP) HD209458b constrain the composition, temperature and density profiles in its upper atmosphere. Thus the observations provide guidance for models that have so far predicted a range of possible conditions. We present the first hydrodynamic escape model for the upper atmosphere that includes all of the detected species in order to explain their presence at high altitudes, and to further constrain the temperature and velocity profiles. This model calculates the stellar heating rates based on recent estimates of photoelectron heating efficiencies, and includes the photochemistry of heavy atoms and ions in addition to hydrogen and helium. The composition at the lower boundary of the escape model is constrained by a full photochemical model of the lower atmosphere. We confirm that molecules dissociate near the 1 \u03bcbar level, and find that complex molecular chemistry does not need to be included above this level. We also confirm that diffusive separation of the detected species does not occur because the heavy atoms and ions collide frequently with the rapidly escaping H and H+. This means that the abundance of the heavy atoms and ions in the thermosphere simply depends on the elemental abundances and ionization rates. We show that, as expected, H and O remain mostly neutral up to at least 3Rp, whereas both C and Si are mostly ionized at significantly lower altitudes. We also explore the temperature and velocity profiles, and find that the outflow speed and the temperature gradients depend strongly on the assumed heating efficiencies. Our models predict an upper limit of 8000 K for the mean (pressure averaged) temperature below 3Rp, with a typical value of 7000 K based on the average solar XUV flux at 0.047 AU. We use these temperature limits and the observations to evaluate the role of stellar energy in heating the upper atmosphere.", "measurement_extractions": [ { "quantity": "1 \u03bcbar", "unit": "\u03bcbar", "measured_entity": "molecules", "measured_property": "dissociate" }, { "quantity": "up to at least 3Rp", "unit": "Rp", "measured_entity": "H and O", "measured_property": "emain mostly neutral" }, { "quantity": "8000 K", "unit": "K", "measured_entity": "models predict", "measured_property": "mean (pressure averaged) temperature" }, { "quantity": "3Rp", "unit": "Rp", "measured_entity": "models predict", "measured_property": null }, { "quantity": "7000 K", "unit": "K", "measured_entity": "models predict", "measured_property": "mean (pressure averaged) temperature" }, { "quantity": "0.047 AU", "unit": "AU", "measured_entity": "models predict", "measured_property": "average solar XUV flux" } ], "split": "train", "docId": "S0019103512004009-2821", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The effect of changing the heating efficiency on the velocity profile is quite dramatic. As \u03b7net ranges from 0.1 to 1 (with the average solar flux), the velocity at the upper boundary increases from 2.6 km s\u22121 to 25 km s\u22121. However, the velocity does not increase linearly with stellar flux or without a bound \u2013 in the 100\u00d7 case the velocity at the upper boundary is only 30 km s\u22121. An interesting qualitative feature of the solutions is that the sonic point moves to a lower altitude with increasing heating efficiency or stellar flux. With \u03b7net = 0.1 the isothermal sonic point is located above the upper boundary whereas with \u03b7net = 1 it is located at 4Rp. This behavior is related to the temperature gradient and it is discussed further in Section 3.1.3. Basically the sonic point, when it exists, moves further from the planet as the high altitude heating rate decreases.", "measurement_extractions": [ { "quantity": "from 0.1 to 1", "unit": null, "measured_entity": "heating efficiency", "measured_property": "\u03b7net" }, { "quantity": "increases from 2.6 km s\u22121 to 25 km s\u22121", "unit": "km s\u22121", "measured_entity": "upper boundary", "measured_property": "velocity" }, { "quantity": "100\u00d7", "unit": "\u00d7", "measured_entity": "case", "measured_property": null }, { "quantity": "30 km s\u22121", "unit": "km s\u22121", "measured_entity": "upper boundary", "measured_property": "velocity" }, { "quantity": "0.1", "unit": null, "measured_entity": "heating efficiency", "measured_property": "\u03b7net" }, { "quantity": "1", "unit": null, "measured_entity": "\u03b7net", "measured_property": null }, { "quantity": "4Rp", "unit": "Rp", "measured_entity": "isothermal sonic point", "measured_property": "located" } ], "split": "train", "docId": "S0019103512004009-3976", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The substellar tide is included in the C3 model. We included it mainly to compare our results with previous models (Garcia Munoz, 2007; Penz et al., 2008; Murray-Clay et al., 2009). The substellar tide is not a particularly good representation of the stellar tide in a globally averaged sense. In reality, including tides in the models is much more complicated than simply considering the substellar tide (e.g., Trammell et al., 2011). Compared to the C1 model, the maximum temperature in the C3 model is cooler by \u223c1000 K and at high altitudes the C3 model is cooler by 1000\u20132000 K. The velocity is faster and hence adiabatic cooling is also more efficient. The substellar tide drives supersonic escape (see also, Penz et al., 2008) and the sonic point in the C3 model is at a much lower altitude than in the C1 model (see Section 3.1.3). However, it is not clear how the sonic point behaves as a function of latitude and longitude. Given that the tide is also likely to induce horizontal flows, it cannot be included accurately in 1D models.", "measurement_extractions": [ { "quantity": "\u223c1000 K", "unit": "K", "measured_entity": "C3 model", "measured_property": "maximum temperature" }, { "quantity": "1000\u20132000 K", "unit": "K", "measured_entity": "C3 model", "measured_property": "maximum temperature" } ], "split": "train", "docId": "S0019103512004009-4350", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The H/H+ transition in the MC09 model occurs near 1.4Rp. If we replace the gray approximation with the full solar spectrum in this model, the H/H+ transition moves higher to 2\u20133Rp. This is because photons with different energies penetrate to different depths in the atmosphere, extending the heating profile in altitude around the heating peak. This is why the temperature at the 30 nbar level in the C2 model is 3800 K and not 1000 K. In order to test the effect of higher temperatures in the lower thermosphere, we extended the MC09 model to p0 = 1 \u03bcbar (with T0 = 1300 K) and again used the full solar spectrum for heating and ionization. With these conditions, the H/H+ transition moves up to 3.4Rp, in agreement with the C2 model. We conclude that the unrealistic boundary conditions and the gray approximation adopted by Murray-Clay et al. (2009) and Guo (2011) lead to an underestimated overall density of H and an overestimated ion fraction. Thus their density profiles yield a H Lyman \u03b1 transit depth of the order of 2\u20133% i.e., not significantly higher than the visible transit depth.", "measurement_extractions": [ { "quantity": "near 1.4Rp", "unit": "Rp", "measured_entity": "H/H+", "measured_property": "transition" }, { "quantity": "2\u20133Rp", "unit": "Rp", "measured_entity": "H/H+", "measured_property": "transition" }, { "quantity": "30 nbar", "unit": "nbar", "measured_entity": "C2 model", "measured_property": "level" }, { "quantity": "3800 K", "unit": "K", "measured_entity": "C2 model", "measured_property": "temperature" }, { "quantity": "1000 K.", "unit": "K", "measured_entity": "C2 model", "measured_property": "temperature" }, { "quantity": "1 \u03bcbar", "unit": "\u03bcbar", "measured_entity": "MC09 model", "measured_property": "p0" }, { "quantity": "1300 K", "unit": "K", "measured_entity": "MC09 model", "measured_property": "T0" }, { "quantity": "3.4Rp", "unit": "Rp", "measured_entity": "H/H+", "measured_property": "transition" }, { "quantity": "2\u20133%", "unit": "%", "measured_entity": "H Lyman \u03b1 transit depth", "measured_property": null } ], "split": "train", "docId": "S0019103512004009-5033", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We calculated the collision frequencies based on the C2 model, and found that collisions with neutral H dominate the transport of heavy neutral atoms such as O below 3.5Rp. At altitudes higher than this, collisions with H+ are more frequent. In Paper II we demonstrate that a mass loss rate of 6 \u00d7 106 kg s\u22121 is required to prevent diffusive separation of O (the heaviest neutral species detected so far) in the thermosphere. The mass loss rate in our models is M\u0307>107kgs-1 and thus O is dragged along to high altitudes by H. On the other hand, collisions with H+ dominate the transport of heavy ions such as Si+ as long as the ratio [H+]/[H] \u2273 10\u22124 (Paper II). This explains why Coulomb collisions in our models are more frequent than heavy ion\u2013H collisions at almost all altitudes apart from the immediate vicinity of the lower boundary. These collisions are much more efficient in preventing diffusive separation than collisions with neutral H.", "measurement_extractions": [ { "quantity": "below 3.5Rp", "unit": "Rp", "measured_entity": "collisions with neutral H dominate the transport of heavy neutral atoms", "measured_property": null }, { "quantity": "6 \u00d7 106 kg s\u22121", "unit": "kg s\u22121", "measured_entity": "mass", "measured_property": "oss rate" }, { "quantity": ">107kgs-1", "unit": "kgs-1", "measured_entity": "models", "measured_property": "M\u0307" }, { "quantity": "\u2273 10\u22124", "unit": null, "measured_entity": "[H+]/[H]", "measured_property": "ratio" } ], "split": "train", "docId": "S0019103512004009-5271", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The Wet Chemistry Laboratory (WCL) on the Phoenix Lander provided in situ measurements of the composition of soluble salts in the martian soil. Soluble sulfate was present at 1.3 \u00b1 0.5 wt.% (Kounaves et al., 2010b), along with cations of sodium, potassium, calcium and magnesium. The most surprising result was the presence of perchlorate (ClO4-) at an inferred concentration in the soil of \u223c0.5 wt.% (Hecht et al., 2009; Kounaves et al., 2010a). The dominance of Mg(ClO4)2 is consistent with simulations of evaporation and freezing at the Phoenix landing site (Marion et al., 2010); however, further analysis of data from the WCL suggests that Ca(ClO4)2 may be the dominant form of perchlorate (Kounaves et al., 2012). The perchlorate-sensitive electrode in the WCL experiment was also sensitive to nitrate, but it was 1000 times more sensitive to perchlorate. Thus, the methodology precluded the detection of nitrate because the signal would have required a mass of nitrate that exceeded the mass of the sample (Hecht et al., 2009). Recently, the MSL mission has also confirmed the presence of perchlorate using pyrolysis as part of the SAM experiment (Steininger et al., 2013). Specifically, pyrolysis showed release of chloromethane and O2 from heated soil samples at the Rocknest location, which is consistent with the decomposition of perchlorate (Sutter et al., 2013). If all of the evolved O2 was released from perchlorate, then the samples contained a comparable amount of perchlorate to the samples at the Phoenix landing site (Leshin et al., 2013). Furthermore, reanalysis of the Viking thermal volatilization experiments suggest \u2a7d1.6% perchlorate at both Viking 1 and Viking 2 landing sites (Navarro-Gonzalez et al., 2010); however, this has been subject to some debate (Biemann and Bada, 2011). Native perchlorate has also recently been measured in the martian meteorite EETA79001, albeit at a level <1 ppm by mass (Kounaves et al., 2014). Given the various locations of possible detection, perchlorate appears to be ubiquitous on the martian surface.", "measurement_extractions": [ { "quantity": "1.3 \u00b1 0.5 wt.%", "unit": "wt.%", "measured_entity": "Soluble sulfate", "measured_property": null }, { "quantity": "\u223c0.5 wt.%", "unit": "wt.%", "measured_entity": "soil", "measured_property": "perchlorate (ClO4-)" }, { "quantity": "1000 times", "unit": "times", "measured_entity": "perchlorate-sensitive electrode", "measured_property": "sensitive to nitrate" }, { "quantity": "\u2a7d1.6%", "unit": "%", "measured_entity": "Viking 1 and Viking 2 landing sites", "measured_property": "perchlorate" }, { "quantity": "<1 ppm by mass", "unit": "ppm by mass", "measured_entity": "martian meteorite EETA79001", "measured_property": "Native perchlorate" } ], "split": "train", "docId": "S0019103513005058-3154", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Most gases are removed from the atmosphere to the surface according to a prescribed deposition velocity. The deposition velocity is a scaling factor that affects the transport of species from the bulk atmosphere to the surface in the absence of rain. The deposition velocity is coupled to the gas concentrations computed by chemical kinetics. Following Zahnle et al. (2008), we apply a deposition velocity of 0.02 cm s\u22121 to all species, with two exceptions. First, the deposition velocities for O2, H2, and CO are set to zero (following Zahnle et al., 2008). Second, all species with a zero deposition velocity in Catling et al. (2010) are prescribed a zero deposition velocity because we consider them to be nonreactive. These species include NO3, N2O5, N2O, CH3O, CH3ONO, CH3ONO2, CH2ONO2, CH3O2, CH3OH, CH2OOH, Cl2, CH2OH, CH2O2, OClO, ClOO, ClONO, ClNO, ClNO2, CH3OCl, Cl2O2, Cl2O, ClO3, and Cl2O4. The deposition velocity multiplied by the species number density at the lower boundary, in addition to the flux term from eddy diffusion, determines the flux of species to the surface.", "measurement_extractions": [ { "quantity": "0.02 cm s\u22121", "unit": "s\u22121", "measured_entity": "all species", "measured_property": "deposition velocity" }, { "quantity": "two", "unit": null, "measured_entity": "exceptions", "measured_property": null }, { "quantity": "zero", "unit": null, "measured_entity": "O2, H2, and CO", "measured_property": "deposition velocities" }, { "quantity": "zero", "unit": null, "measured_entity": "all species", "measured_property": "deposition velocity" }, { "quantity": "zero", "unit": null, "measured_entity": "all species", "measured_property": "deposition velocity" } ], "split": "train", "docId": "S0019103513005058-3917", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Atmospheric temperatures affect photochemical rate constants and atmospheric water vapor content. These, in turn, affect the chain of reaction rates that lead to the oxidation of Cl to form HClO4. To pinpoint how atmospheric temperatures alter reaction rates, we shift the nominal Mars temperature profile to higher surface temperatures. The shape of the profile is preserved, but the surface temperature is increased from 211 K to 284 K (a temperature increase of \u223c35%), with the latter temperature being more representative of surface temperatures on Earth.", "measurement_extractions": [ { "quantity": "from 211 K to 284 K", "unit": "K", "measured_entity": "surface temperature", "measured_property": "increased" }, { "quantity": "\u223c35%", "unit": "%", "measured_entity": "temperature", "measured_property": "increase" } ], "split": "train", "docId": "S0019103513005058-4098", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We next make several assumptions to calculate the concentration of salts that have accumulated in the soil during the Amazonian eon. We first assume that perchloric acid, sulfate aerosols, nitric acid and pernitric acid have accumulated at a uniform rate. This assumption is valid because the lack of aqueous minerals and very low weathering rates tell us the Amazonian eon on Mars has been characterized by a climate and atmosphere not greatly different from today (Bibring et al., 2006). We next assume a range of soil mixing depths so that salts are distributed throughout the soil column. According to Zent (1998), small post-Noachian impactors have churned the soil on Mars to a 1/e mixing depth of 0.51\u20130.85 m. Taking three e-folding depths, the range would be 1.5\u20132.6 m depth, with a mean \u223c2 m. The last assumption we make is that the soil density is 1 g cm\u22123 (Moore and Jakosky, 1989). Using these assumptions, we calculate the concentrations of anions in the soil for the nominal model and report them in Table 4. These anions must be combined into salts. For perchlorate, the salts may be Mg(ClO4)2 or Ca(ClO4)2 as discussed earlier. The dominant nitrogen-bearing salt is unknown.", "measurement_extractions": [ { "quantity": "1/e", "unit": null, "measured_entity": null, "measured_property": null }, { "quantity": "0.51\u20130.85 m.", "unit": "m", "measured_entity": "impactors have churned the soil on Mars", "measured_property": "1/e mixing depth" }, { "quantity": "three", "unit": null, "measured_entity": "e-folding depths", "measured_property": null }, { "quantity": "1.5\u20132.6 m", "unit": "m", "measured_entity": "three e-folding depths", "measured_property": "depth" }, { "quantity": "\u223c2 m.", "unit": "m", "measured_entity": "three e-folding depths", "measured_property": "depth" }, { "quantity": "1 g cm\u22123", "unit": "g cm\u22123", "measured_entity": "soil", "measured_property": "density" } ], "split": "train", "docId": "S0019103513005058-4158", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The sulfate deposition flux produced in the nominal model is compatible with estimates of the amount of sulfates on Mars. The nominal range (1.0\u20131.7 wt.% SO4) is consistent with 1.3 wt.% soluble sulfate measured at the Phoenix landing site (Kounaves et al., 2010b). The estimates also compare well with an average \u223c6.8 wt.% sulfur as SO3 (2.7 wt.% S) in global soil inferred from elemental abundances measured at various locations on Mars by Spirit, Opportunity, Pathfinder, and Viking Landers. We can also compare the average sulfur content in the top few tens of centimeters of the soil of \u223c4.4 wt.% inferred from Gamma Ray Spectrometer measurements (King and McLennan, 2010). The agreement between model results and data suggest that 0.1% of the terrestrial volcanic gas flux is a good estimate for the volcanic emission rate on Mars 1\u20132 Ga if soil salts derive from volcanic input.", "measurement_extractions": [ { "quantity": "1.0\u20131.7 wt.%", "unit": "wt.%", "measured_entity": "global soil", "measured_property": "SO4" }, { "quantity": "1.3 wt.%", "unit": "wt.%", "measured_entity": "soil", "measured_property": "soluble sulfate" }, { "quantity": "average \u223c6.8 wt.%", "unit": "wt.%", "measured_entity": "global soil", "measured_property": "SO3" }, { "quantity": "2.7 wt.%", "unit": "wt.%", "measured_entity": "global soil", "measured_property": "S" }, { "quantity": "top few tens of centimeters", "unit": "centimeters", "measured_entity": "soil", "measured_property": null }, { "quantity": "\u223c4.4 wt.%", "unit": "wt.%", "measured_entity": "soil", "measured_property": "average sulfur content" }, { "quantity": "0.1%", "unit": "%", "measured_entity": "terrestrial volcanic gas flux", "measured_property": null } ], "split": "train", "docId": "S0019103513005058-4175", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "As stated previously, there is considerable uncertainty in the input rate of odd nitrogen (N and NO) species from the martian ionosphere to the neutral atmosphere (Krasnopolsky, 1993). In his own model of the neutral atmosphere, Krasnopolsky considers cases both with and without input of odd nitrogen from the upper atmosphere (Krasnopolsky, 1993). We vary the input of N and NO into the model, using the nominal case as an upper limit. As these values are decreased, the pernitrate deposition flux drops, which is shown in Fig. 5. The lowest input of odd nitrogen corresponds to 3.5\u20136.1 (\u00d710\u22124) wt.% N accumulated over 3 byr and mixed into 1.5\u20132.6 m of soil.", "measurement_extractions": [ { "quantity": "3.5\u20136.1 (\u00d710\u22124) wt.%", "unit": "wt.%", "measured_entity": "N", "measured_property": "input of odd nitrogen" }, { "quantity": "over 3 byr", "unit": "byr", "measured_entity": "input of odd nitrogen", "measured_property": "accumulated" }, { "quantity": "1.5\u20132.6 m", "unit": "m", "measured_entity": "soil", "measured_property": null } ], "split": "train", "docId": "S0019103513005058-4302", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "To semi-quantitatively assess the sensitivity of the deposition fluxes of salts to temperature, we forced the model temperature profile to higher values by increasing the temperature profile by 35% in 5% increments. Warmer temperatures significantly increase the formation of perchloric acid. Over the range tested, the deposition rate of perchloric acid increases by around six orders of magnitude.", "measurement_extractions": [ { "quantity": "35%", "unit": "%", "measured_entity": "temperature profile", "measured_property": null }, { "quantity": "5%", "unit": "%", "measured_entity": "temperature profile", "measured_property": "increments" }, { "quantity": "around six orders of magnitude", "unit": "orders of magnitude", "measured_entity": "perchloric acid", "measured_property": "deposition rate" } ], "split": "train", "docId": "S0019103513005058-4349", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "To obtain a relatively stable substrate during the measurements, all iron substrates were pre-conditioned in a 10 wt.% NaOH solution overnight to form a thin oxidized surface layer, followed by rinsing with 99.5% pure ethanol and drying with a gentle stream of nitrogen gas prior to use. The alkaline treatment of the iron surface results in formation of hematite (\u03b1-Fe2O3), which will be shown and discussed in relation to Fig. 9. The hematite surface contains different types of surface hydroxyl groups that differ by their coordination to the substrate [26]. The hematite surface is amphoteric due to the possibility of protonization and deprotonization of the surface hydroxyl groups. In neutral solution, the \u03be-potential has been found to be slightly positive [27,28]. The water contact angle on our hematite surface is 69 \u00b1 3\u00b0, and the open circuit potential relative to Ag/AgCl is \u22120.72 V, which is similar to that of iron due to the small thickness of the oxide layer.", "measurement_extractions": [ { "quantity": "10 wt.%", "unit": "wt.%", "measured_entity": "solution", "measured_property": "NaOH" }, { "quantity": "99.5%", "unit": "%", "measured_entity": "pure ethanol", "measured_property": null }, { "quantity": "69 \u00b1 3\u00b0", "unit": "\u00b0", "measured_entity": "water", "measured_property": "contact angle" }, { "quantity": "\u22120.72 V", "unit": "V", "measured_entity": "hematite surface", "measured_property": "open circuit potential" } ], "split": "train", "docId": "S0021979713004438-1401", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Tinnitus is the perception of sounds in the head or ears, usually defined as a ringing, buzzing or whistling sound. Tinnitus can be objective or subjective. Objective tinnitus is caused by sounds generated by an internal biological activity. However, subjective tinnitus is much more common and results from abnormal neural activities which are not formed by sounds [1]. Subjective tinnitus is a common and disturbing phenomenon, with a reported prevalence ranging from 7 to 20% [2\u20135] in the general population, and an estimated 10 year incidence rate in adults aged over 48 years of 13% [6]. Approximately 5% of the population is severely affected by their tinnitus [7], for example experiencing sleep disorders, concentration difficulties, and symptoms of anxiety and depression.", "measurement_extractions": [ { "quantity": "7 to 20%", "unit": "%", "measured_entity": "general population", "measured_property": "prevalence" }, { "quantity": "10 year", "unit": "year", "measured_entity": "ncidence rate", "measured_property": null }, { "quantity": "over 48 years", "unit": "years", "measured_entity": "adults", "measured_property": null }, { "quantity": "13%", "unit": "%", "measured_entity": "adults aged over 48 years", "measured_property": "10 year incidence rate" }, { "quantity": "Approximately 5%", "unit": "%", "measured_entity": "population", "measured_property": "severely affected by their tinnitus" } ], "split": "train", "docId": "S0022399913003358-931", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Personality characteristics previously reported to be associated with tinnitus include hysteria and hypochondriasis [9,12], introversion [13], withdrawal [9], and emotional isolation [14]. Additionally, particular cognitive strategies, for example, dysfunctional and catastrophic thoughts can increase patients' emotional distress and perceived tinnitus severity, and are thought to be closely related to personality factors [15]. Neuroticism is expressed as \u201cindividual differences in the tendency to experience negative, distressing emotions\u201d [16] (p. 301). At one extreme, individuals are characterized by high levels of vulnerability to experience negative emotions, including sadness, fear, anxiety, anger, frustration, and insecurity [17]. At the other end of the spectrum, individuals who score low in neuroticism are more emotionally stable and less reactive to stress. Neuroticism has been associated with adverse outcomes in various health conditions, including increased likelihood of morbidity in those with testicular cancer [18], and an increased likelihood of arthritis, kidney/liver disease, and diabetes in the general population [19]. There is evidence that neurotic traits are stronger in tinnitus patients [20], particularly in those with higher levels of tinnitus annoyance, and recent evidence that neuroticism may predict the development of severe tinnitus in patients already experiencing some tinnitus [21]. In a cross-sectional sample of 530 participants (50% with chronic tinnitus), Bartels and colleagues [22] studied the role of type D personality (the tendency towards negative affectivity and social inhibition) on health-related quality of life and self-reported tinnitus-related distress. Tinnitus patients with type D personality reported greater tinnitus-related distress and poorer health-related quality of life compared to those with other personality types. The authors concluded that some personality characteristics are associated with having tinnitus and are likely to contribute to its perceived severity.", "measurement_extractions": [ { "quantity": "530 participants", "unit": "participants", "measured_entity": "cross-sectional sample", "measured_property": null }, { "quantity": "50%", "unit": "%", "measured_entity": "530 participants", "measured_property": "with chronic tinnitus" } ], "split": "train", "docId": "S0022399913003358-943", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "SQUID magnetometry measurements revealed that both samples 1 and 2 exhibit weak temperature independent paramagnetic behaviour (typically \u03c7M\u223c5\u00d710\u22124 emu mol\u22121) between 5 and 300 K. This behaviour is commensurate with other alkaline earth nitride halides and suggests either weakly paramagnetic materials or intrinsically diamagnetic materials with very small levels of alkaline earth metal impurities (below the detection limit of PXD and PND) [15,16]. (A plot of \u03c7M vs. T for 1 and 2 is available as Supplementary information.)", "measurement_extractions": [ { "quantity": "between 5 and 300 K", "unit": "K", "measured_entity": "samples 1 and 2", "measured_property": "temperature" } ], "split": "train", "docId": "S0022459611006116-1448", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Refined crystallographic parameters for (1) from PXD and PND at 298 K.", "measurement_extractions": [ { "quantity": "298 K.", "unit": "K", "measured_entity": "Refined crystallographic parameters for (1) from PXD and PND", "measured_property": null } ], "split": "train", "docId": "S0022459611006116-547", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "\u25ba We examine a high resolution multi-proxy physical properties from two marine cores. \u25ba Little correlation between physical proxies and climate in early Holocene \u25ba Reworking probable cause of poor correlation in Early Holocene \u25ba Possible anthropogenic influence on sedimentation in the last 200 years", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "marine cores", "measured_property": null }, { "quantity": "last 200 years", "unit": "years", "measured_entity": "sedimentation", "measured_property": null } ], "split": "train", "docId": "S0025322712001600-2230", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Analysis of the diffraction data was conducted by measuring peak intensity as peak area using Bruker Diffrac Plus EVA-12.0 software. Estimates of mineral composition were made by a reference intensity ratio method based on factors calculated with the Newmod programme as described in Hillier (2003). Illite crystallinity was measured using the full width at half maximum (FHWM) of the 001 basal illite peak and integral breadth (I Breadth) of the same peak (K\u00fcbler and Jaboyedoff, 2000). Both measurements are measured as values of \u22062\u00b0\u03b8 and show identical trends (Alizai et al., 2012). Because our cores are < 9 m long, post-depositional burial diagenesis should not be a significant factor in clay mineral composition. Where clay mineral values are greater than 10% uncertainty is estimated as better than 5% weight at the 95% confidence level (Hillier, 2003). Clay mineral estimates are shown in Table 1.", "measurement_extractions": [ { "quantity": "< 9 m", "unit": "m", "measured_entity": "cores", "measured_property": "long" }, { "quantity": "greater than 10%", "unit": "%", "measured_entity": "clay mineral", "measured_property": "values" }, { "quantity": "better than 5%", "unit": "%", "measured_entity": "uncertainty", "measured_property": "weight" }, { "quantity": "95%", "unit": "%", "measured_entity": "values", "measured_property": "confidence level" } ], "split": "train", "docId": "S0025322712001600-2406", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The aim of the current investigation was to determine whether fungal and bacterial species richness would affect the development of soil structural properties (e.g. aggregate stability and pore size) over a 7-month period and establish whether changes in genetic composition would be brought about by the presence of roots (either mycorrhizal or non-mycorrhizal). Since the premise of the investigation was to quantify the relationship between biological richness and soil structural changes over time, the soils were not pre-incubated prior to the start of the experiment. Therefore, microbial communities were allowed to develop during the course of the 7 month experiment either in the presence of mycorrhizal or non-mycorrhizal roots, or in unplanted soil, thereby allowing root associated changes in community development to be measured. Others, for example Griffiths et al. (2001) and Wertz et al. (2006), incubated soils for 9 or 4.8 months respectively to allow microbial communities to develop a similar biomass before biodiversity/function relationships were studied. In this investigation, the progression of soil structural development together with microbial compositional changes over time and in tandem with root development was characterised.", "measurement_extractions": [ { "quantity": "over a 7-month", "unit": "month", "measured_entity": "current investigation", "measured_property": "period" }, { "quantity": "7 month", "unit": "month", "measured_entity": "experiment", "measured_property": "course" }, { "quantity": "9", "unit": "months", "measured_entity": "soils", "measured_property": "incubated" }, { "quantity": "4.8 months", "unit": "months", "measured_entity": "soils", "measured_property": "incubated" } ], "split": "train", "docId": "S0031405612000728-1621", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In the present study, both aggregate stability and repellency were reduced in month 7; specifically the degree of reduction in repellency was less in the mycorrhizal soils than in the non-mycorrhizal soils. In the mycorrhizal soils, aggregate water repellency was also negatively correlated with bacterial (and fungal) TRF richness but positively correlated with root size and microbial biomass-C. It is likely that mycorrhizal hyphae contributed to the microbial biomass-C measured here which might explain why microbial biomass-C was not a factor in the model explaining repellency in the NM soils. In the mycorrhizal soils the relationship between microbial biomass-C and aggregate stability was negative, whilst it was positive for repellency. The GLM regressions used data for all 7 months but the system was dynamic across the months. For example, aggregate stability was greater in the mycorrhizal soils in month 3, yet repellency increased in months 5 and 7. The positive relationship observed between per cent root length colonised and microbial biomass-C is likely to be the result of increasing hyphal length in the soil, or possibly an enhancement of other microbial species too, since internal AMF root colonisation may not reflect the extraradical hyphal biomass. Aggregate turnover rates range from 4 to 88 days (De Gryze et al. 2005, 2006); an increase in aggregate stability observed here over a 60 day period (from the first to third month harvest) and an increase in aggregate water repellency over a 120 day period (from the first to fifth month harvest) is comparable to that observed by others.", "measurement_extractions": [ { "quantity": "7", "unit": null, "measured_entity": "months", "measured_property": null }, { "quantity": "range from 4 to 88 days", "unit": "days", "measured_entity": "Aggregate turnover", "measured_property": "rates" }, { "quantity": "over a 60 day", "unit": "day", "measured_entity": "aggregate stability", "measured_property": "period" }, { "quantity": "over a 120 day", "unit": "day", "measured_entity": "aggregate water repellency", "measured_property": "period" } ], "split": "train", "docId": "S0031405612000728-1639", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Principal component analysis of fungal TRFs for (A) all seven months combined and (B) for month 7 only.", "measurement_extractions": [ { "quantity": "seven", "unit": null, "measured_entity": "months", "measured_property": null } ], "split": "train", "docId": "S0031405612000728-769", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Inspection of the datasets from a large number of orbits showed that it was convenient to locate the Ion Composition Boundary (ICB), which marks the transition between the shocked solar wind and the planetary plasma (e.g. Martinecz et al., 2008), by considering the mass channel number at which the largest number of ions was observed in each 192 s cycle. Data from times at which the mass channel number of the maximum ion count was 15 or less were taken to correspond to altitudes below the ICB. These data were then considered for further analysis. For example, in the data set for 9th August 2008 shown in Fig. 1, the data between 05:28 UT and 05:47 UT were interpreted as being from inside the ICB. These data are shown within the pink box in Fig. 1, and it was these data that were considered for further analysis in this particular example.", "measurement_extractions": [ { "quantity": "192 s", "unit": "s", "measured_entity": "cycle", "measured_property": null }, { "quantity": "15 or less", "unit": null, "measured_entity": "maximum ion count", "measured_property": "mass channel number" }, { "quantity": "9th August 2008", "unit": null, "measured_entity": "data set", "measured_property": null }, { "quantity": "between 05:28 UT and 05:47 UT", "unit": "UT", "measured_entity": "data", "measured_property": null } ], "split": "train", "docId": "S0032063312002437-627", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The first panel of Fig. 1 shows an example of a peaked electron distribution. The time interval of the ELS spectrogram is 12 min, revealing a clear \u201cinverted-V\u201d structure over a 2-min interval at the centre of the spectrogram. The DEF energy spectrum at the right shows a positive gradient below the energy of the peak, which is at approximately 60 eV. This corresponds to a peak of similar energy when the energy spectrum is plotted in PSD, but is not shown here. By comparing the energy spectrum of the electron feature to those from the different regions around Mars we find the origin is most likely from the solar wind. Comparing to the modelled Maxwellian distributions show the presence of an accelerated peak as well as an added contribution of heated electrons. This indicates the electron feature could be some way between penetrating solar wind and magnetosheath electrons. Evidence of electrons being accelerated and heated has been found in previous analysis of \u201cinverted-V\u201d electrons and is observed as preferably transverse to the magnetic field at low altitudes (Lundin et al., 2006b; Dubinin et al., 2009).", "measurement_extractions": [ { "quantity": "12 min", "unit": "min", "measured_entity": "ELS spectrogram", "measured_property": "time interval" }, { "quantity": "2-min", "unit": "min", "measured_entity": "clear \u201cinverted-V\u201d structure", "measured_property": "over" }, { "quantity": "approximately 60 eV.", "unit": "eV", "measured_entity": "peak", "measured_property": "energy" } ], "split": "train", "docId": "S0032063312003054-1990", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "It is possible to explain such observations in general if we consider MEX to be above a region that has accelerated electrons upwards, in which case heavy-ions could also be accelerated downwards. This may explain the appearance of the very low DEF of heavy-ions at \u223c400eV. However, due to the finite gyro-radius effect it is also plausible that the general behaviour of the heavy-ions flowing away from Mars may not change, even when passing an acceleration region. The gyroradius of heavy-ions with energies around \u223c10eV at the location of the accelerated electron signature is around 100 km, which is of a similar spatial scale to the horizontal size of a closed crustal magnetic field line at 400 km. Therefore, it is possible the heavy-ions do not remain in the acceleration region long enough to experience its effects.", "measurement_extractions": [ { "quantity": "\u223c400eV.", "unit": "eV", "measured_entity": "heavy-ions", "measured_property": "DEF" }, { "quantity": "\u223c10eV", "unit": "eV", "measured_entity": "heavy-ions", "measured_property": "energies" }, { "quantity": "around 100 km", "unit": "km", "measured_entity": "heavy-ions", "measured_property": "gyroradius" }, { "quantity": "400 km", "unit": "km", "measured_entity": "closed crustal magnetic field line", "measured_property": "horizontal size" } ], "split": "train", "docId": "S0032063312003054-2264", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The combination of electron and heavy-ion energy distributions that make up category-2, suggestive of a downward current and category-4 with both up-going electrons and heavy-ions, is only considered for those electron precipitation signatures that have electron energy distributions with a significant asymmetry. For these signatures, up-going electrons and heavy-ions are the most common combination, while the combination of up-going electrons and down-going heavy-ions occur almost half as often. Even after discounting upward net flux of electrons from those signatures with isotropic electron energy distribution, category-4 still make up the second largest group and when added together with category-2 occur on 10% of MEX orbits.", "measurement_extractions": [ { "quantity": "10%", "unit": "%", "measured_entity": "MEX orbits", "measured_property": "category-4 still make up the second largest group and when added together with category-2" } ], "split": "train", "docId": "S0032063312003054-2467", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Fig. 5 also shows an acceleration of heavy-ions between \u223c01:25:00 UT and 01:25:40 UT, prior to the first signature of electron precipitation shown in the ELS spectrogram. Similar acceleration of heavy-ions is found around a number of other events of electron precipitation signatures. Further analysis of this type of acceleration of heavy-ions will be left for future work. However, we refer to these events as showing a \u201cperipheral acceleration\u201d of heavy-ions. We have included the identification of these events in Table 1 to compare with the results of the energy distribution categories.", "measurement_extractions": [ { "quantity": "between \u223c01:25:00 UT and 01:25:40 UT", "unit": "UT", "measured_entity": "heavy-ions", "measured_property": "acceleration" } ], "split": "train", "docId": "S0032063312003054-2483", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Out of the total 689 events of electron precipitation signatures, 85 were observed with a concurrent acceleration of heavy-ions. This accounts for 12% of precipitation signatures occurring on \u223c5% of MEX orbits. Only 37 events of electron precipitation signatures were observed with a peripheral acceleration of heavy-ions. This makes the peripheral acceleration of heavy-ions less common occurring on just 5% of precipitation signatures and on \u223c2% of MEX orbits. Therefore, it is quite rare to observe accelerated beams of heavy-ions during signatures of electron precipitation at Mars.", "measurement_extractions": [ { "quantity": "689", "unit": null, "measured_entity": "events of electron precipitation signatures", "measured_property": null }, { "quantity": "85", "unit": null, "measured_entity": "events of electron precipitation signatures", "measured_property": null }, { "quantity": "12%", "unit": "%", "measured_entity": "precipitation signatures occurring on \u223c5% of MEX orbits", "measured_property": "concurrent acceleration of heavy-ions" }, { "quantity": "\u223c5%", "unit": "%", "measured_entity": "MEX orbits", "measured_property": null }, { "quantity": "37", "unit": null, "measured_entity": "events of electron precipitation signatures", "measured_property": null }, { "quantity": "5%", "unit": "%", "measured_entity": "precipitation signatures", "measured_property": "peripheral acceleration of heavy-ions" }, { "quantity": "\u223c2%", "unit": "%", "measured_entity": "MEX orbits", "measured_property": "peripheral acceleration of heavy-ions" } ], "split": "train", "docId": "S0032063312003054-2501", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We interpret our FAC density profiles by considering the corresponding precipitating electron energy fluxes, as shown in Fig. 6(b). Fluxes are plotted as functions of latitude. The line style code and labels are the same as in Fig. 6(a), the latitudinal size of a HST ACS-SBC pixel is indicated by the dark grey rectangle and the grey solid line indicates the limit of present HST detectability (~1kR; Cowley et al., 2007). We begin by comparing profiles for case ES with EF, which are almost identical and both have maxima at ~74\u00b0 latitude, equivalent to the location of the \u2018main auroral oval\u2019, and at ~80\u00b0, the boundary between open (region I) and closed field lines (region II). Therefore, we would expect a fairly bright auroral oval of ~88kR for case ES and ~79kR for case EF. The electron energy flux for case EF (~7.85mWm\u22122) is ~10% smaller than case ES (~8.8mWm\u22122) due to \u03a9T(ES)>\u03a9T(EF) leading to a smaller flow shear. Our model also predicts the possibility of observable polar emission (region II/I boundary) of ~15kR for both cases ES and EF. However, this region is strongly dependent on the plasma flow model used and poorly constrained by observations.", "measurement_extractions": [ { "quantity": "~1kR", "unit": "kR", "measured_entity": "limit of present HST detectability", "measured_property": null }, { "quantity": "~74\u00b0 latitude", "unit": "\u00b0 latitude", "measured_entity": "case ES with EF", "measured_property": "maxima" }, { "quantity": "~80\u00b0", "unit": "\u00b0", "measured_entity": "case ES with EF", "measured_property": "boundary between open (region I) and closed field lines (region II)" }, { "quantity": "~88kR", "unit": "kR", "measured_entity": "case ES", "measured_property": "auroral oval" }, { "quantity": "~79kR", "unit": "kR", "measured_entity": "case EF", "measured_property": "auroral oval" }, { "quantity": "(~7.85mWm\u22122", "unit": "mWm\u22122", "measured_entity": "case EF", "measured_property": "electron energy flux" }, { "quantity": "~10%", "unit": null, "measured_entity": "case EF", "measured_property": "electron energy flux" }, { "quantity": "~8.8mWm\u22122", "unit": "mWm\u22122", "measured_entity": "case ES", "measured_property": "electron energy flux" }, { "quantity": "~15kR", "unit": "kR", "measured_entity": "model also predicts", "measured_property": "observable polar emission (region II/I boundary)" } ], "split": "train", "docId": "S0032063313003218-6651", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The particles also significantly toughened the epoxy polymer even at about \u2212100 \u00b0C.", "measurement_extractions": [ { "quantity": "about \u2212100 \u00b0C", "unit": "\u00b0C", "measured_entity": "particles", "measured_property": "toughened" } ], "split": "train", "docId": "S0032386113005454-2008", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The glass transition temperature, Tg, of all the bulk samples was measured using dynamic-mechanical thermal analysis (DMTA) with a Q800 DMA from TA Instruments, UK. A double-cantilever mode at 1 Hz was employed using test specimens 60 \u00d7 10 \u00d7 3 mm3 in size. The temperature range used was \u2212100 \u00b0C to 200 \u00b0C with a heating rate of 4 \u00b0C/min. The value of Tg was determined at the peak value of tan \u03b4. The number average molecular weight between cross-links, Mnc, was also calculated from the equilibrium modulus in the rubbery region, Er, using [20](1)Mnc=q\u03c1RT/Erwhere T is the temperature in K at which the value of Er was taken, \u03c1 is the density of the epoxy at the temperature T, the term R is the universal gas constant, and q is the front factor. As the density of the epoxy was only measured at room temperature, the value of the front factor, q, was taken to be 0.725, as in previous work [21]. The density, \u03c1, of the epoxy was measured at room temperature according to BS ISO 1183-1 Method A [22] to be 1.20 g/m3 at 20 \u00b0C.", "measurement_extractions": [ { "quantity": "1 Hz", "unit": "Hz", "measured_entity": "double-cantilever mode", "measured_property": null }, { "quantity": "60 \u00d7 10 \u00d7 3 mm3", "unit": "mm3", "measured_entity": "test specimens", "measured_property": "size" }, { "quantity": "\u2212100 \u00b0C to 200 \u00b0C", "unit": "\u00b0C", "measured_entity": "temperature range", "measured_property": null }, { "quantity": "4 \u00b0C/min", "unit": "\u00b0C/min", "measured_entity": "heating rate", "measured_property": null }, { "quantity": "room temperature", "unit": null, "measured_entity": "epoxy", "measured_property": null }, { "quantity": "0.725", "unit": null, "measured_entity": "front factor", "measured_property": null }, { "quantity": "room temperature", "unit": null, "measured_entity": "epoxy", "measured_property": null }, { "quantity": "1.20 g/m3", "unit": "g/m3", "measured_entity": "epoxy", "measured_property": "density" }, { "quantity": "20 \u00b0C", "unit": "\u00b0C", "measured_entity": "room temperature", "measured_property": null } ], "split": "train", "docId": "S0032386113005454-2055", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A tensile modulus of 3.19 \u00b1 0.10 GPa was measured for the unmodified epoxy polymer. The modulus decreased approximately linearly with increasing CSR content to 1.96 \u00b1 0.08 GPa when 20 wt% of S-CSR particles were added, see Table 1. Similar results were reported by Giannakopoulos et al. [30] using the same formulation of epoxy polymer but with different CSR particles.", "measurement_extractions": [ { "quantity": "3.19 \u00b1 0.10 GPa", "unit": "GPa", "measured_entity": "unmodified epoxy polymer", "measured_property": "tensile modulus" }, { "quantity": "to 1.96 \u00b1 0.08 GPa", "unit": "GPa", "measured_entity": "epoxy polymer", "measured_property": "modulus" }, { "quantity": "20 wt%", "unit": "wt%", "measured_entity": "S-CSR particles", "measured_property": "were added" } ], "split": "train", "docId": "S0032386113005454-2308", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The mean room-temperature values of the compressive true yield stress, \u03c3yc, compressive true fracture stress, \u03c3fc, and compressive true fracture strain, \u03b3f, are summarised in Table 2. The tensile yield stress is calculated from the measured compressive yield stress [39]. The addition of S-CSR particles reduces the compressive true yield stress, as expected, due to the relative softness of the polysiloxane rubber. The values decreased approximately linearly with increasing S-CSR particle content, see Fig. 4. At 20 \u00b0C, a value of 111 MPa was measured for the unmodified epoxy polymer, which also reveals that the unmodified epoxy should have the highest strength, if the effect of defects is excluded, when the test is conducted in uniaxial tension. The lowest value of the compressive true yield stress was measured to be 63 MPa for the 20 wt% S-CSR-modified epoxy polymer.", "measurement_extractions": [ { "quantity": "20 \u00b0C", "unit": "\u00b0C", "measured_entity": "unmodified epoxy polymer", "measured_property": null }, { "quantity": "111 MPa", "unit": "MPa", "measured_entity": "unmodified epoxy polymer", "measured_property": null }, { "quantity": "63 MPa", "unit": "MPa", "measured_entity": "compressive true yield stress", "measured_property": "lowest value" }, { "quantity": "20 wt%", "unit": "wt%", "measured_entity": "S-CSR-modified epoxy polymer", "measured_property": null } ], "split": "train", "docId": "S0032386113005454-2601", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "At room temperature, the fracture surfaces of the S-CSR particle-modified polymers also showed crack forking and feather markings. However, these fracture surfaces are rougher than those of the unmodified epoxy, and scanning electron micrographs of the deformation zone for the 10 wt% and 20 wt% S-CSR-modified epoxy polymers are shown in Fig. 13. The fracture surfaces are covered with cavitated S-CSR particles, which can be identified as the circular features in Fig. 13. The cavitation process causes the originally solid rubber particles to deform into a rubbery shell surrounding a void. The mean diameter of these cavities was measured to be 0.296 \u03bcm. This is significantly larger than the mean diameter of the S-CSR particles measured from the AFM images, which was 0.18 \u03bcm. This observation clearly reveals that plastic void growth of the epoxy polymer has followed cavitation of the S-CSR particles. This is one of the main toughening mechanisms for such thermoset polymers toughened by the presence of well-dispersed rubber particles. Essentially, the cavitation of the particle creates voids which relieve the triaxial stress-state ahead of the crack tip and so enable plastic void growth to occur far more readily in the epoxy polymer. Cavitation, as opposed to particle debonding, will occur when the rubber particle is strongly bonded to the surrounding polymer. Indeed, based on the FEG-SEM observations, the core to shell adhesion must also be relatively high for the S-CSR particles, as no debonding is observed. For the low-temperature results, the fracture surfaces of the particle-modified polymers are very similar to the samples tested at room temperature, see Figs. 14 and 15. Indeed, all of the S-CSR particles cavitated, even at \u2212109 \u00b0C, although the size of the cavities is reduced at low temperatures, which indicates a lesser extent of plastic void growth in the epoxy polymer.", "measurement_extractions": [ { "quantity": "room temperature", "unit": null, "measured_entity": "S-CSR particle-modified polymers", "measured_property": null }, { "quantity": "10 wt% and 20 wt%", "unit": "%", "measured_entity": "S-CSR-modified epoxy polymers", "measured_property": null }, { "quantity": "0.296 \u03bcm", "unit": "\u03bcm", "measured_entity": "cavities", "measured_property": "mean diameter" }, { "quantity": "0.18 \u03bcm", "unit": "\u03bcm", "measured_entity": "S-CSR particles", "measured_property": "mean diameter" }, { "quantity": "room temperature", "unit": null, "measured_entity": "samples", "measured_property": null }, { "quantity": "\u2212109 \u00b0C", "unit": "\u00b0C", "measured_entity": "S-CSR particles", "measured_property": "cavitated" } ], "split": "train", "docId": "S0032386113005454-2865", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "An energy-based criterion was used to predict debonding of the particles. The method used has been fully described elsewhere [49] and essentially it proposes that the criterion for debonding is based upon the energy released by the debonding process. To obtain the parameters needed for this energy-based criterion, a finite-element analysis modelling study has been used to derive the change in strain-energy arising from the cavitation process, with the addition of the strain-energy stored in the particle prior to debonding. The applied stress used for these simulations was derived from experimental observations. Namely, as implied above, the debonding of the silica nanoparticles from the epoxy matrix polymer appears to take place during the elastic deformation region and, as shown in Table 1, the yield stress for all modified epoxy polymers is approximately equal, irrespective of particle size. It has therefore been assumed that the debonding takes place at an applied uniaxial stress of about 70 MPa, which equates to a hydrostatic stress at the crack tip of about 210 MPa. Thus, the finite-element analysis simulations were analysed for an applied hydrostatic stress of 210 MPa.", "measurement_extractions": [ { "quantity": "about 70 MPa", "unit": "MPa", "measured_entity": "debonding", "measured_property": "applied uniaxial stress" }, { "quantity": "about 210 MPa", "unit": "MPa", "measured_entity": "debonding", "measured_property": "hydrostatic stress" }, { "quantity": "210 MPa", "unit": "MPa", "measured_entity": "finite-element analysis simulations", "measured_property": "applied hydrostatic stress" } ], "split": "train", "docId": "S0032386113009889-2123", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We conclude that the biological components contributing to RS at the forest site of San Rossore were mostly from heterotrophic origin, and constrained within the top 20\u201330 cm of the soil profile. Our results reflected the soil respiration processes which characterize a water- and nutrient-limited forest sites such as San Rossore (Rosenkranz et al., 2006). In a recent study on the soil organic carbon of six Mediterranean forest sites in Italy, Chiti et al. (2010) reported that the pine forest of San Rossore had the lowest SOC accumulation within the top 20 cm of soil profile, and predicted that by the end of the second commitment period of the Kyoto protocol (2013\u20132017) would become a source of SOC.", "measurement_extractions": [ { "quantity": "20\u201330 cm", "unit": "cm", "measured_entity": "soil profile", "measured_property": null }, { "quantity": "six", "unit": null, "measured_entity": "Mediterranean forest sites in Italy", "measured_property": null }, { "quantity": "20 cm", "unit": "cm", "measured_entity": "soil profile", "measured_property": null } ], "split": "train", "docId": "S0038071711004354-2573", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Despite the lack of correlation between aggregate stability and AMF, rapid growth of G. mosseae hyphae suggested by Jansa et al. (2008) might be expected to result in alterations in soil structure relative to hyphae of other slower growing species. Work by Giovannetti et al. (2001, 2004) demonstrated that AMF hyphae frequently anastamose and could result in a large network of interconnecting hyphae. Within the current experiment, colonisation by G. mosseae resulted in a higher proportion of small soil pores to large ones and reduced distances between neighbouring pores. A soil with a greater proportion of small pores (\u223c0.4 mm2) will retain water more effectively than a soil with larger pores; furthermore, small pores act as nutrient-rich habitats for soil microflora (Nunan et al., 2001). In contrast, G. intraradices led to soil with a greater number of large pores and all combinations of G. intraradices resulted in greater distances between neighbouring pores. Bearden (2001) concluded, in a study of soil\u2013water characteristics, that AMF hyphae resulted in soils gaining groups of small pores which corroborates the current findings, although it is interesting that different Glomus species affect the size and distance between pores differently. Bearden (2001) used a combination of six Glomus species (including G. mosseae) in her investigation of vertisols. In contrast, Milleret et al. (2009) found no evidence of G. intraradices forming small diameter structural pores and the larger size of the pores relative to the AMF led them to conclude that pore development was an indirect effect of the mycorrhiza. Since the extraradical hyphae had a smaller diameter than the pores, the authors suggested that hyphal penetration of pores induced root exudation and increased microbial activity. The data in the current investigation show that plant roots, possibly by influencing the associated microbial biomass and not colonisation by AMF per se, positively affected aggregate stability. Previous studies (e.g. Bedini et al., 2009) showed that aggregate stability is affected by the plant-fungal system rather than by plant root metabolism. Hallett et al. (2009) performed an elegant experiment in which mycorrhizal-deficient and \u2018wild type\u2019 tomato plants were grown in an agricultural field soil amended with G. mosseae and G. intraradices. These authors demonstrated that aggregate stability and porosity were increased in planted soils irrespective of the plant type (and mycorrhizal status) and they conclude that roots drive the stabilisation of soil structure. Their experiment lasted for 84 days and during this time the soil experienced wet\u2013dry cycles which may have encouraged microbial populations to produce polysaccharides which in turn enhanced aggregate production. However in the current study, individual AMF species and combinations of co-occurring AMF species caused short-term effects on pore dynamics which may result in long-term impacts on porosity and aggregate stability. Whilst it has been shown before that different AMF species may affect aggregate stability (Schreiner et al., 1997), the focus here is on the effects of the mycelium in addition to the mycorrhiza. No correlations were observed for AMF hyphal abundance and soil physical properties, demonstrating that the differences observed may relate to growth patterns and mycelium architecture rather than density, or perhaps to AMF-specific altered C-exudation since AM fungi are known to produce extracellular soil proteins such as glomalin (Wright and Upadhyaya, 1996). The data presented here demonstrate that different AMF species within the same genus may have contrasting effects on soil pore characteristics which could alter the micro-physical habitat for other soil organisms.", "measurement_extractions": [ { "quantity": "\u223c0.4 mm2", "unit": "mm2", "measured_entity": "small pores", "measured_property": null }, { "quantity": "84 days", "unit": "days", "measured_entity": "experiment", "measured_property": "lasted" } ], "split": "train", "docId": "S0038071712001010-1044", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "AM fungal inoculum as a single factor was significant (P < 0.001) with distinct species combinations resulting in different levels of root colonisation. The individual inocula resulted in similar levels of colonisation (9.9%\u201316.2%; LSD = 7.01) irrespective of species, therefore valid comparisons with and between the mixed species could be made. The highest percentage colonisation was observed in the two-species mixture of G. geosporum plus G. mosseae (25.4%), followed by the combination of all three species (23.8%). Percentage colonisation by G. geosporum plus G. mosseae in combination appeared to be additive relative to that observed by the species individually (16.2%, G. geosporum; 9.9% G. mosseae). Colonisation was markedly reduced when G. mosseae and G. intraradices were paired (7.5%), particularly compared to the performance of G. intraradices individually (14.2%). Arbuscules followed a similar pattern to hyphal colonisation, with the G. geosporum and G. mosseae combination containing the most arbuscules (data not shown). No mycorrhizal colonisation was observed in the uninoculated plant roots.", "measurement_extractions": [ { "quantity": "< 0.001", "unit": null, "measured_entity": "AM fungal inoculum as a single factor", "measured_property": "P" }, { "quantity": "9.9%\u201316.2%", "unit": "%", "measured_entity": "levels of colonisation", "measured_property": null }, { "quantity": "7.01", "unit": null, "measured_entity": "levels of colonisation", "measured_property": "LSD" }, { "quantity": "25.4%", "unit": "%", "measured_entity": "two-species mixture of G. geosporum plus G. mosseae", "measured_property": "percentage colonisation" }, { "quantity": "23.8%", "unit": "%", "measured_entity": "combination of all three species", "measured_property": "percentage colonisation" }, { "quantity": "16.2%", "unit": "%", "measured_entity": "G. geosporum", "measured_property": "Percentage colonisation" }, { "quantity": "9.9%", "unit": "%", "measured_entity": "G. mosseae", "measured_property": "Percentage colonisation" }, { "quantity": "7.5%", "unit": "%", "measured_entity": "G. mosseae and G. intraradices", "measured_property": "Colonisation" }, { "quantity": "14.2%", "unit": "%", "measured_entity": "G. intraradices", "measured_property": "Colonisation" } ], "split": "train", "docId": "S0038071712001010-918", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "None of the mycorrhizal fungi when inoculated individually increased plant biomass. However, root growth responded positively to the G. mosseae plus G. intraradices combination, resulting in a mycorrhizal growth response of 115% on a whole plant basis and 169% on a root only basis (P = 0.001) (Fig. 2). The fungal combination that resulted in the lowest percentage root length colonised induced the highest mycorrhizal growth response.", "measurement_extractions": [ { "quantity": "115%", "unit": "%", "measured_entity": "mycorrhizal growth", "measured_property": "G. mosseae plus G. intraradices combination" }, { "quantity": "169%", "unit": "%", "measured_entity": "mycorrhizal growth", "measured_property": "G. mosseae plus G. intraradices combination" }, { "quantity": "0.001", "unit": null, "measured_entity": "mycorrhizal growth response", "measured_property": "P" } ], "split": "train", "docId": "S0038071712001010-944", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A conceptual picture on what was a healthy or a diseased soil could be perceived by looking at the responses of majority of fungal community members to soil variables. Majority (55%) of OTUs in healthy soils were stimulated (encouraged) by a certain set of soil variables but the majorities (63%) in diseased soils were inhibited (discouraged) (Table 1). For a complex natural community, it is impossible that every environmental element encourages every community member: majority makes senses. With this view, a healthy soil was likely a soil with variables that encouraged majority of fungal community, whereas a diseased soil was a soil with variables that discouraged majorities. Any society that encourages majority of its members will be more likely to become a vigorous and successful society.", "measurement_extractions": [ { "quantity": "55%", "unit": "%", "measured_entity": "OTUs in healthy soils", "measured_property": "stimulated (encouraged) by a certain set of soil variables" }, { "quantity": "63%", "unit": "%", "measured_entity": "OTUs", "measured_property": "were inhibited (discouraged)" } ], "split": "train", "docId": "S0038071713001971-1388", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The following are the supplementary data related to this article:Fig. S1The yield records of recent two years. The yield level between dashed lines indicated the yield range of local farmers who practice rotations.", "measurement_extractions": [ { "quantity": "two years", "unit": "years", "measured_entity": "yield records", "measured_property": null } ], "split": "train", "docId": "S0038071713001971-1427", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "As an example, the open source calculator bc contains 9438 lines of code represented by 7538 SDG vertices. The B-MSG for bc, shown in Fig. 3a, contains a large plateau that spans almost 70% of the MSG. Under the assumption that same slice size implies the same slice, this indicates a large same-slice cluster. However, \u201czooming\u201d in reveals that the cluster is actually composed of several smaller clusters made from slices of very similar size. The tolerance implicit in the visual resolution used to plot the MSG obscures this detail.", "measurement_extractions": [ { "quantity": "9438 lines of code", "unit": "lines of code", "measured_entity": "open source calculator bc", "measured_property": "contains" }, { "quantity": "7538 SDG vertices", "unit": "SDG vertices", "measured_entity": "9438 lines of code", "measured_property": "represented" }, { "quantity": "almost 70%", "unit": "%", "measured_entity": "MSG", "measured_property": "large plateau" } ], "split": "train", "docId": "S016412121300188X-4069", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The accuracy of hash function H is given as Hashed Slice Precision, HSP = UH/US . A precision of 1.00 (US = UH) means the hash function is 100% accurate (i.e., it produces a unique hash value for every distinct slice) whereas a precision of 1/US means that the hash function produces the same hash value for every slice leaving UH = 1.", "measurement_extractions": [ { "quantity": "1.00", "unit": null, "measured_entity": "hash function", "measured_property": "precision" }, { "quantity": "100%", "unit": "%", "measured_entity": "hash function", "measured_property": "accurate" }, { "quantity": "1/", "unit": null, "measured_entity": "hash function", "measured_property": "precision" }, { "quantity": "1", "unit": null, "measured_entity": "UH", "measured_property": null } ], "split": "train", "docId": "S016412121300188X-4392", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "To assess if a program includes a large coherent cluster, requires making a judgement concerning what threshold constitutes large. Following prior empirical work (Binkley and Harman, 2005; Harman et al., 2009; Islam et al., 2010a,b), a threshold of 10% is used. In other words, a program is said to contain a large coherent cluster if 10% of the program's SDG vertices produce the same backward slice as well as the same forward slice.", "measurement_extractions": [ { "quantity": "10%", "unit": "%", "measured_entity": "threshold", "measured_property": null }, { "quantity": "10%", "unit": "%", "measured_entity": "program's SDG vertices", "measured_property": "produce the same backward slice as well as the same forward slice" } ], "split": "train", "docId": "S016412121300188X-4436", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Table 4 shows the statistics for the five largest clusters of acct. Column 1 gives the cluster number, where 1 is the largest and 5 is the 5th largest cluster measured using the number of vertices. Columns 2 and 3 show the size of the cluster as a percentage of the program's vertices and actual vertex count, as well as the line count. Columns 4 and 5 show the number of files and functions where the cluster is found. The cluster sizes range from 11.4% to 2.4%. These five clusters can be readily identified in the Heat-Map visualization (not shown) of decluvi. The rest of the clusters are very small (less than 2% or 30 vertices) in size and are thus of little interest.", "measurement_extractions": [ { "quantity": "five", "unit": null, "measured_entity": "largest clusters of acct", "measured_property": null }, { "quantity": "range from 11.4% to 2.4%", "unit": "%", "measured_entity": "cluster", "measured_property": "sizes" }, { "quantity": "five", "unit": null, "measured_entity": "clusters", "measured_property": null }, { "quantity": "less than 2%", "unit": "%", "measured_entity": "rest of the clusters", "measured_property": null }, { "quantity": "30 vertices", "unit": "vertices", "measured_entity": "rest of the clusters", "measured_property": null } ], "split": "train", "docId": "S016412121300188X-4545", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The next case study uses indent to further support the answer found for RQ4 in the acct case study. The characteristics of indent are very different from those of acct as indent has a very large dominant coherent cluster (52%) whereas acct has multiple smaller clusters with the largest being 11%. We include indent as a case study to ensure that the answer for RQ4 is derived from programs with different cluster profiles and sizes giving confidence as to the generality of the answer.", "measurement_extractions": [ { "quantity": "52%", "unit": "%", "measured_entity": "very large dominant coherent cluster", "measured_property": null }, { "quantity": "11%", "unit": "%", "measured_entity": "largest", "measured_property": null } ], "split": "train", "docId": "S016412121300188X-4617", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Indent has one extremely large coherent cluster that spans 52.1% of the program's vertices. The cluster is formed of vertices from 54 functions spread over 7 source files. This cluster captures most of the logical functionalities of the program. Out of the 54 functions, 26 begin with the common prefix of \u201chandle_token\u201d. These 26 functions are individually responsible for handling a specific token during the formatting process. For example, handle_token_colon, handle_token_comma, handle_token_comment, and handle_token_lbrace are responsible for handling the colon, comma, comment, and left brace tokens, respectively.", "measurement_extractions": [ { "quantity": "one", "unit": null, "measured_entity": "coherent cluster", "measured_property": null }, { "quantity": "52.1%", "unit": "%", "measured_entity": "program's vertices", "measured_property": "coherent cluster" }, { "quantity": "54", "unit": null, "measured_entity": "functions", "measured_property": null }, { "quantity": "7", "unit": null, "measured_entity": "source files", "measured_property": null }, { "quantity": "54", "unit": null, "measured_entity": "functions", "measured_property": null }, { "quantity": "26", "unit": null, "measured_entity": "functions", "measured_property": null }, { "quantity": "26", "unit": null, "measured_entity": "functions", "measured_property": null } ], "split": "train", "docId": "S016412121300188X-4640", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "As coherent clusters are composed of both backward and forward slices, the stability of the backward slice profile itself does not guarantee the stability of coherent cluster profile. The remainder of this section looks at how the clustering profile is affected by bug fixes. Fig. 20 shows individual SCGs for each version of barcode. As coherent clusters are dependent on both backward and forward slices, such clusters will be more sensitive to changes in dependences within the program. The SCGs show that from the initial version barcode-0.90 there were two coherent clusters in the system. The smaller one is around 10% of the code while the larger is around 40% of the code. As the system evolved and went through various modifications and enhancements, the number of clusters and the profile of the clusters remained consistent other than its scaled growth with the increase in program size. It is also evident that during evolution of the system, the enhancement code or newly added code formed part of the larger cluster. This is why in the later stages of the evolution we see an increase in the size of the largest cluster, but not the smaller one.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "coherent clusters", "measured_property": null }, { "quantity": "around 10%", "unit": "%", "measured_entity": "code", "measured_property": "coherent clusters" }, { "quantity": "around 40%", "unit": "%", "measured_entity": "code", "measured_property": "coherent clusters" } ], "split": "train", "docId": "S016412121300188X-4937", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "As an answer to RQ7, this study finds that unless there is significant refactoring of the system, coherent cluster profiles remain stable during system evolution and thus captures the core architecture of the program in all three case studies. Future work will replicate this longitudinal study on a large code corpus to ascertain whether this stability holds for other programs.", "measurement_extractions": [ { "quantity": "three", "unit": null, "measured_entity": "case studies", "measured_property": null } ], "split": "train", "docId": "S016412121300188X-5038", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "This paper extends our previous work which introduced coherent dependence clusters (Islam et al., 2010b) and decluvi (Islam et al., 2010a). Previous work established the existence of coherent dependence clusters and detailed the functionalities of the visualization tool. This paper extends previous work in many ways, firstly by introducing an efficient hashing algorithm for slice approximation. This improves on the precision of previous slice approximation from 78% to 95%, resulting in precise and accurate clustering. The coherent cluster existence study is extended to empirically validate the results by considering 30 production programs. Additional case studies show that coherent clusters can help reveal the structure of a program and identify structural defects. We also introduce the notion of inter-cluster dependence which will form the base of reverse engineering efforts in future. Finally, we also present studies which show the lack of correlation between coherent clusters and bug fixes and show that coherent clusters remain surprisingly stable during system evolution.", "measurement_extractions": [ { "quantity": "from 78% to 95%", "unit": "%", "measured_entity": "precision of previous slice", "measured_property": "improves" }, { "quantity": "30", "unit": null, "measured_entity": "production programs", "measured_property": null } ], "split": "train", "docId": "S016412121300188X-5066", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "S. pneumoniae, M. catarrhalis and H. influenzae are the most common pathogens implicated in OME, and all are capable of forming biofilms [33,42]. However, rather than focusing just on those three bacteria, this study cultured effusions on a wide range of different media for prolonged time periods in order to capture as many isolates as possible. Interestingly, coagulase negative staphylococci (CoNS), Veillonella spp. and S. aureus were the three commonest pathogens isolated in this study. CoNS were long thought to be non-pathogenic commensals, but with the recognition of their biofilm-forming capacity have emerged as the leading cause of biomaterials-related infection [43,43]. S. lugdunensis, isolated here on three occasions, in particular has been implicated in endocarditis, wound infection, and implant-related infection as well as otitis media, behaving more like S. aureus than other CoNS [45]. Other CoNS have also been previously implicated in otitis media, with a recent study finding that they account for 60% of bacteria isolated from OME [46,47]. Veillonella is a Gram-negative anaerobe that inhabits the mouth and upper respiratory tract, forms biofilms [48] and has previously been found in middle ear disease [49,50]. S. aureus also forms biofilms and has been identified in middle ear disease [51,52]. Although most of the bacteria in Table 2 have previously been isolated in middle ear disease, to the best of the authors\u2019 knowledge Flavimonas oryzihabitans, Vibrio metschnikovii and Gemella haemolysans have not been implicated previously.", "measurement_extractions": [ { "quantity": "three", "unit": null, "measured_entity": "bacteria", "measured_property": null }, { "quantity": "three", "unit": null, "measured_entity": "commonest pathogens", "measured_property": null }, { "quantity": "three occasions", "unit": "occasions", "measured_entity": "S. lugdunensis", "measured_property": "isolated" }, { "quantity": "60%", "unit": "%", "measured_entity": "bacteria isolated from OME", "measured_property": "CoNS" } ], "split": "train", "docId": "S0165587612003680-1078", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A possible explanation for the discrepancy between high PCR-positive rate and low culture-positive rate in OME is the involvement of biofilms in the progression of this pathology [20]. Indeed, biofilms have been identified on human middle ear mucosa in children with OME and/or recurrent AOM in more than 90% of cases, but not in any control samples studied [12]. In addition to tissue surfaces, biofilms have also been identified attached to mucus [21,22] and attach in vitro to collagen gel matrix [23]. In OME, biofilms may be attached to mucus as well as mucosa, thus providing the inflammatory stimulus leading to a middle ear effusion [10,13,24].", "measurement_extractions": [ { "quantity": "more than 90%", "unit": "%", "measured_entity": "children with OME and/or recurrent AOM", "measured_property": "biofilms have been identified on human middle ear mucosa" } ], "split": "train", "docId": "S0165587612003680-953", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Differences between adults and children were explored on a per-patient (rather than per-ear) basis; where data existed for two ears, the per-patient analysis was carried out using the criteria of at least one ear being culture/confocal positive and at least one ear containing biofilms. Children appeared to have a greater number of culture-positive, confocal-positive, and biofilm results than adults (54.3% vs. 14.3%, 82.9% vs. 57.1%, and 67.9% vs. 0%, respectively). However, only the presence of biofilms reached statistical significance (Fisher's exact test p = 0.02).", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "ears", "measured_property": null }, { "quantity": "at least one", "unit": null, "measured_entity": "ear", "measured_property": null }, { "quantity": "at least one", "unit": null, "measured_entity": "ear", "measured_property": null }, { "quantity": "54.3%", "unit": "%", "measured_entity": "Children", "measured_property": "culture-positive" }, { "quantity": "14.3%", "unit": "%", "measured_entity": "adults", "measured_property": "culture-positive" }, { "quantity": "82.9%", "unit": "%", "measured_entity": "Children", "measured_property": "confocal-positive" }, { "quantity": "57.1%", "unit": "%", "measured_entity": "adults", "measured_property": "confocal-positive" }, { "quantity": "67.9%", "unit": "%", "measured_entity": "Children", "measured_property": "biofilm" }, { "quantity": "0%", "unit": "%", "measured_entity": "adults", "measured_property": "biofilm" }, { "quantity": "0.02", "unit": null, "measured_entity": "Fisher's exact test", "measured_property": "p" } ], "split": "train", "docId": "S0165587612003680-998", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "FTIR spectra of (a) PLGA fibres, (b) HA nanopowder, (c) PLGA\u2013HA composite fibres, and (d) sintered PLGA\u2013HA fibres. The pure PLGA spectrum shows the C3O characteristic bands in the region 1065\u20131280 cm\u22121. The spectrum of HA nanopowder reveals the characteristic peak assigned to PO43\u2212: \u03bd1 vibration mode at about 964 cm\u22121, \u03bd3 vibration mode at 1031 cm\u22121 and 1091 cm\u22121 (asymmetric) respectively. The spectrum of untreated PLGA\u2013HA 50% displays both characteristics of PLGA and HA. However the spectrum of CaP fibres shows the loss of the characteristics of PLGA (C3O bands), suggesting the successful thermal degradation of the polymer, while the characteristics of HA (PO43\u2212) remain present.", "measurement_extractions": [ { "quantity": "1065\u20131280 cm\u22121", "unit": "cm\u22121", "measured_entity": "pure PLGA spectrum", "measured_property": "C3O characteristic bands" }, { "quantity": "about 964 cm\u22121", "unit": "cm\u22121", "measured_entity": "spectrum of HA nanopowder", "measured_property": "\u03bd1 vibration mode" }, { "quantity": "1031 cm\u22121", "unit": "cm\u22121", "measured_entity": "spectrum of HA nanopowder", "measured_property": "\u03bd3 vibration mode" }, { "quantity": "1091 cm\u22121", "unit": "cm\u22121", "measured_entity": "spectrum of HA nanopowder", "measured_property": "\u03bd3 vibration mode" }, { "quantity": "50%", "unit": null, "measured_entity": "PLGA\u2013HA", "measured_property": null } ], "split": "train", "docId": "S0167577X13006393-399", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Sintering of pure HA particles is usually reported to occur above 1000 \u00b0C. The choice of the sintering temperature is important as it has an effect on the properties of the resulting sample. Most investigators agree that pure HA (ratio CaP=1.67) is stable in an air and argon atmosphere at temperatures upto 1200 \u00b0C [18\u201320]. However, decomposition of HA at temperatures as low as 800 \u00b0C has been observed for calcium deficient HA samples [19].", "measurement_extractions": [ { "quantity": "above 1000 \u00b0C", "unit": "\u00b0C", "measured_entity": "Sintering of pure HA particles", "measured_property": "reported to occur" }, { "quantity": "1.67", "unit": null, "measured_entity": "CaP", "measured_property": "ratio" }, { "quantity": "upto 1200 \u00b0C", "unit": "\u00b0C", "measured_entity": "pure HA (ratio CaP=1.67) is stable in an air and argon atmosphere", "measured_property": "temperatures" }, { "quantity": "low as 800 \u00b0C", "unit": "\u00b0C", "measured_entity": "decomposition of HA", "measured_property": "at temperatures" } ], "split": "train", "docId": "S0167577X13006393-644", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The XPS data clearly indicates that the HA nanoparticles used in this experiment are deficient in calcium (Ca/P ratio=1.37). This could explain why the HA decomposition occurs before 1200 \u00b0C since deficient HA start their decomposition at temperatures lower than pure HA (ratio=1.67) [19]. In the literature, this decomposition is described as partial although the reason remains unknown. The commonly accepted decomposition reaction for deficient HA isCa10(PO4)6(OH)2\u21923Ca3(PO4)2+CaO+H2O", "measurement_extractions": [ { "quantity": "1.37", "unit": null, "measured_entity": "Ca/P", "measured_property": "ratio" }, { "quantity": "before 1200 \u00b0C", "unit": "\u00b0C", "measured_entity": "HA decomposition", "measured_property": "occurs" }, { "quantity": "1.67", "unit": null, "measured_entity": "deficient HA start their decomposition at temperatures lower than pure HA", "measured_property": "ratio" } ], "split": "train", "docId": "S0167577X13006393-787", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "One-dimensional composition profiles of Fe, Cr, Ni, Mo, Mn, Ti, Nb, C, Si, and P across SD 1 and SD 2 are shown in (a) and (d), respectively, and (b) and (e) show the same at higher magnification (12\u00d7). These elemental compositions were obtained along the arrow of 4 nm width shown in the insets in (c) and (f).", "measurement_extractions": [ { "quantity": "4 nm", "unit": "nm", "measured_entity": "arrow", "measured_property": "width" } ], "split": "train", "docId": "S0167577X14001256-389", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The 3DAP observations were performed using a Cameca Instruments Inc. laser-assisted local electrode 3DAP (LEAP-3000XHR). To reduce the probability of tip fracture during 3DAP evaporation of the atomic layers, we used field evaporation with a laser pulse instead of an electric field pulse. The 3DAP observation conditions were as follows: laser energy of 0.5\u20130.6 nJ, a laser-pulse repetition rate of 250 kHz, a DC bias voltage of 4\u20137 kV, and a specimen temperature of 55 K. The DC voltage was automatically controlled to keep a constant evaporation rate of 0.4% at constant energy of the laser pulse. The Ga+-irradiated regions by FIB around the top of the thinned tip were fractured before 3DAP observation at a DC voltage range of 2\u20134 kV. The computer program IVAS3.6.1 (Cameca Instruments Inc.) was used to analyze the data. Orientations of the screw dislocations were defined with high accuracy on the basis of {111} planes of stacking faults, which were observed while mapping the same atom. Details of the methods were explained in our previous paper [7].", "measurement_extractions": [ { "quantity": "0.5\u20130.6 nJ", "unit": "nJ", "measured_entity": "3DAP observation conditions", "measured_property": "laser energy" }, { "quantity": "250 kHz", "unit": "kHz", "measured_entity": "3DAP observation conditions", "measured_property": "laser-pulse repetition rate" }, { "quantity": "4\u20137 kV", "unit": "kV", "measured_entity": "3DAP observation conditions", "measured_property": "DC bias voltage" }, { "quantity": "55 K.", "unit": "K", "measured_entity": "3DAP observation conditions", "measured_property": "specimen temperature" }, { "quantity": "0.4%", "unit": "%", "measured_entity": "constant evaporation rate", "measured_property": null }, { "quantity": "range of 2\u20134 kV", "unit": "kV", "measured_entity": "3DAP observation", "measured_property": "DC voltage" } ], "split": "train", "docId": "S0167577X14001256-517", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For each of the 7 initial orientations, a case, numbered 4 in Table 2, was run using a 0.563 m square plate of thickness 0.025 m. The density of the plate was adjusted to ensure that at the mean horizontal wind flow, the Tachikawa number, K and Froude number Fr are the same as in the number 1 cases. The result is two sets of cases, A1 to G1 and A4 to G4, with the same set of initial orientations, aspect ratio and flow parameter but considerably lower thickness ratio, \u03c4=t/L, and non-dimensionalised mass moment of inertia about the Z-axis, \u0394zz, in the A4 to G4 cases. Similarly a fifth case, numbered 5 in Table 2 is run for each initial orientation, and the plate's K, FrL, \u03c4 and \u0394zz are kept constant relative to cases numbered 4, but the width of the plate, B is increased to give a higher aspect ratio.", "measurement_extractions": [ { "quantity": "7", "unit": null, "measured_entity": "initial orientations", "measured_property": null }, { "quantity": "0.563 m", "unit": "m", "measured_entity": "square plate", "measured_property": null }, { "quantity": "0.025 m.", "unit": "m", "measured_entity": "square plate", "measured_property": "thickness" }, { "quantity": "two", "unit": null, "measured_entity": "sets of cases", "measured_property": null } ], "split": "train", "docId": "S0167610512002292-3187", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The results from three different simulations are presented here\u2014one with a 1.0 kg plate, a second with a 6.35 kg plate (typical of a clay roofing tile), and a third with a 12 kg plate. The latter is rather heavy for a typical roof tile, but was used to test the range of applicability of the model. A uniform inflow condition is used, with the wind speed of 35 m/s (126 km/h) is used, which is within the range of full-scale failure wind speeds typically discussed by Visscher and Kopp (2007).", "measurement_extractions": [ { "quantity": "three", "unit": null, "measured_entity": "different simulations", "measured_property": null }, { "quantity": "1.0 kg", "unit": "kg", "measured_entity": "plate", "measured_property": null }, { "quantity": "6.35 kg", "unit": "kg", "measured_entity": "plate", "measured_property": null }, { "quantity": "12 kg", "unit": "kg", "measured_entity": "plate", "measured_property": null }, { "quantity": "35 m/s", "unit": "m/s", "measured_entity": "inflow condition", "measured_property": "wind speed" }, { "quantity": "126 km/h", "unit": "km/h", "measured_entity": "inflow condition", "measured_property": "wind speed" } ], "split": "train", "docId": "S0167610512002292-3305", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Fig. 8 shows the mean difference between wind speeds measured by the lidar and sonic anemometer against anemometer wind speed. The data was divided into bins of 1 m s\u22121. The error bars show the standard error of the data in each bin. The mean difference between the two instruments remains fairly constant, with the lidar overestimating the wind speed by between 0 and 0.5 m s\u22121. At wind speeds greater than 20 m s\u22121 the lidar appears to be overestimating the wind speed. Further observations would be required to determine whether this is an accurate reflection of the performance of this method at very high wind speeds.", "measurement_extractions": [ { "quantity": "1 m s\u22121", "unit": "m s\u22121", "measured_entity": "bins", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "instruments", "measured_property": null }, { "quantity": "between 0 and 0.5 m s\u22121", "unit": "m s\u22121", "measured_entity": "wind", "measured_property": "speed" }, { "quantity": "greater than 20 m s\u22121", "unit": "m s\u22121", "measured_entity": "wind", "measured_property": "speeds" } ], "split": "train", "docId": "S0167610513001001-1751", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "\u03b8=tan\u22121(w/U) against mean wind speed (gate midpoint=180 m). Median wind speed is 7.83 m s\u22121.", "measurement_extractions": [ { "quantity": "180 m", "unit": "m", "measured_entity": "gate midpoint", "measured_property": null }, { "quantity": "7.83 m s\u22121", "unit": "m s\u22121", "measured_entity": "wind", "measured_property": "speed" } ], "split": "train", "docId": "S0167610513001001-739", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "At x=0.64 H (about two-third of the way into the street, Fig. 6d), a similar picture applies, but the core of the vortex is further up and there is now a strong flow in the lower part of the canyon towards the lee of the building. This would cause any material in the street canyon (which, for example, might have come from advection along the street from upstream) to be driven into the recirculation region.", "measurement_extractions": [ { "quantity": "0.64 H", "unit": "H", "measured_entity": "x", "measured_property": null } ], "split": "train", "docId": "S0167610513002729-1062", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The situation for the 45\u00b0 case is more interesting (Fig. 9). Here the release is in an intersection and the flow is oblique. In this snapshot, at z=0.23 H the whole plume is blown into the canyon region between the cubes in the y-direction and none in the x-direction. Note that the flow is constantly changing and that this is literally a snapshot. In a different snapshot this might of course be different and on average the plume would be equally likely to be blown into the gap in the x-direction. A little further up, at z=0.52 H, we see that much of the material is now caught in the wake region. A little still comes from the horizontal location of the original source. Higher still, at z=0.80 H, most of the material is concentrated in the region between the cubes. Nothing comes from the horizontal location of the original source. The highest concentration is around the centre of the gap, but somewhat away from the mean location of the core of the recirculation behind the upstream cube. This indicates the possible release of material trapped further down in the recirculation. These visualisations suggest the idea that transfer from the original source location to the wake occurs predominantly at low levels, whereas the re-release of material occurs mainly at higher levels. It would be of interest to test this hypothesis quantitatively in future studies. Above the array, material appears to be coming from the wake region, which therefore acts as a \u2018secondary source\u2019.", "measurement_extractions": [ { "quantity": "45\u00b0", "unit": "\u00b0", "measured_entity": "case", "measured_property": null }, { "quantity": "0.23 H", "unit": "H", "measured_entity": "z", "measured_property": null }, { "quantity": "0.52 H", "unit": "H", "measured_entity": null, "measured_property": null }, { "quantity": "0.80 H", "unit": "H", "measured_entity": null, "measured_property": null } ], "split": "train", "docId": "S0167610513002729-1127", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Although each node has 18 cores, the intended use does not require any specific number of cores, merely (any) one to provide node control and some others to run the application. Indeed for cost purposes it is intended to use some flawed devices; yield estimates suggest that this may improve the usability of manufactured dice from 50% to around 80%. Based on the area use of the die the majority of flaws may be expected to be in local memories; these may leave a core degraded but still usable although the simplest action is still to shut it down.", "measurement_extractions": [ { "quantity": "18 cores", "unit": "cores", "measured_entity": "node", "measured_property": null }, { "quantity": "from 50% to around 80%", "unit": "%", "measured_entity": "manufactured dice", "measured_property": "improve the usability" } ], "split": "train", "docId": "S0167819113001051-1247", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "As expected our design did not deadlock whereas a conventional unit deadlocked roughly 2% of the times that a glitch appears. This is very significant as a single deadlock has the potential to cripple a link permanently (until the whole system is rebooted). Given the communication-intensive application model supported by SpiNNaker this would mean a network becoming highly degraded very quickly if glitches appeared.", "measurement_extractions": [ { "quantity": "roughly 2%", "unit": "%", "measured_entity": "times that a glitch appear", "measured_property": "conventional unit deadlocked" } ], "split": "train", "docId": "S0167819113001051-1550", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "\u2022SOC stocks decreased by 12.4% in Costa Rica and 0.13% in Nicaragua after establishment of coffee AFS.\u2022SOC stocks increased in the top 10 cm of soil; greater reduction occurred at 20\u201340 cm.\u2022Organic management caused a greater increase in 0\u201310 cm SOC but did not influence reduction at depth.\u2022Shade type effects on SOC were smaller; no significant difference between shaded and unshaded coffee.\u2022SOC stocks tend to converge on a level determined by site environment during establishment.", "measurement_extractions": [ { "quantity": "12.4%", "unit": "%", "measured_entity": "SOC stocks", "measured_property": "decreased" }, { "quantity": "0.13%", "unit": "%", "measured_entity": "SOC stocks", "measured_property": "decreased" }, { "quantity": "top 10 cm", "unit": "cm", "measured_entity": "soil", "measured_property": null }, { "quantity": "20\u201340 cm", "unit": "cm", "measured_entity": "soil", "measured_property": null }, { "quantity": "0\u201310 cm", "unit": "cm", "measured_entity": "soil", "measured_property": null } ], "split": "train", "docId": "S0167880913001229-1021", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Organic management caused a greater increase in 0\u201310 cm SOC but did not influence reduction at depth.", "measurement_extractions": [ { "quantity": "0\u201310 cm", "unit": "cm", "measured_entity": null, "measured_property": null } ], "split": "train", "docId": "S0167880913001229-1033", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "There was a difference between the experiments in the two countries in the effects of main-plot (shade) and subplot (coffee management) treatments on total SOC stocks (Mg C ha\u22121). In Costa Rica the ANOVA showed significant (p < 0.01) overall effects of both on the change in SOC stock at 0\u201310 cm depth over the 9-year period. However, in deeper soil only the shade treatment effect remained significant and there was an additional significant (p < 0.01) effect of initial C concentration at the 20\u201340 cm depth. In contrast, in Nicaragua the ANOVA showed no significant effects of main-plot treatment or subplot treatment or of initial C concentration at any soil depth.", "measurement_extractions": [ { "quantity": "< 0.01", "unit": null, "measured_entity": "ANOVA", "measured_property": "p" }, { "quantity": "0\u201310 cm", "unit": "cm", "measured_entity": "SOC stock", "measured_property": "depth" }, { "quantity": "over the 9-year", "unit": "year", "measured_entity": "change in SOC stock", "measured_property": "period" }, { "quantity": "20\u201340 cm", "unit": null, "measured_entity": "SOC stock", "measured_property": "depth" }, { "quantity": "< 0.01", "unit": null, "measured_entity": "effect of initial C concentration", "measured_property": "p" } ], "split": "train", "docId": "S0167880913001229-1225", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The difference between management treatments is likely to be due to the application of organic fertilisers (at up to 11.25 Mg ha\u22121 year\u22121), as no significant differences were found between these subplot treatments for total inputs of above-ground biomass to the soil in the form of senescent leaf litter and pruned material (p = 0.24). Further, there was a positive correlation between the mass of organic fertiliser inputs and changes in 0\u201310 cm depth SOC (r2 = 0.18, p < 0.01). Both conventional and organic managements showed a consistent decline in SOC stocks at the two lower soil depths with no significant between-treatment differences (Fig. 3). Changes in total 0\u201340 cm depth SOC stock showed no significant correlations with either pruning or organic fertiliser inputs.", "measurement_extractions": [ { "quantity": "up to 11.25 Mg ha\u22121 year\u22121", "unit": "Mg ha\u22121 year\u22121", "measured_entity": "organic fertilisers", "measured_property": null }, { "quantity": "0.24", "unit": null, "measured_entity": "no significant differences", "measured_property": "p" }, { "quantity": "0\u201310 cm", "unit": "cm", "measured_entity": "SOC", "measured_property": "depth" }, { "quantity": "0.18", "unit": null, "measured_entity": "positive correlation", "measured_property": "r2" }, { "quantity": "< 0.01", "unit": null, "measured_entity": "positive correlation", "measured_property": "p" }, { "quantity": "two", "unit": null, "measured_entity": "lower soil depths", "measured_property": null }, { "quantity": "0\u201340 cm", "unit": "cm", "measured_entity": "SOC", "measured_property": "depth" } ], "split": "train", "docId": "S0167880913001229-1304", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Gibberellin levels are determined by genes that encode positive regulators such as GIBBERELLIN INSENSITIVE DWARF 1 (GID1) [27] or negative regulators such as GA-INSENSITIVE (GAI) and REPRESSOR OF GA-1 (RGA) [25]. Hybrid poplar (P. tremula \u00d7 P. alba) over-expressing gai (mutant gene with a 51-bp in-frame deletion) had narrow compact crowns with shorter main stems and branches than the wild type. By contrast, those that expressed the wild-type form of the gene (GAI) had phenotypes that were not significantly different from the non-transgenic controls, except for early and high frequency of flowering (Table 1) [25]. DELLA proteins, which contain an N-terminal DELLA (asp-glu-leu-leu-ala) domain essential for GA-dependent proteasomal degradation, repress GA responses [28]. The GID1-GA complex down-regulates DELLA repressor proteins, consequently stimulating plant growth and development [29]. Hybrid aspen (P. tremula \u00d7 P. tremuloides) over-expressing GID1 were about 40% taller than the wild type after nine weeks in the greenhouse [19].", "measurement_extractions": [ { "quantity": "51-bp", "unit": "bp", "measured_entity": "mutant gene", "measured_property": "n-frame deletion" }, { "quantity": "about 40%", "unit": "%", "measured_entity": "Hybrid aspen (P. tremula \u00d7 P. tremuloides) over-expressing GID1", "measured_property": "taller" } ], "split": "train", "docId": "S0168945213001805-3964", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "RuBisCO evolved before the development of photosynthesis, under CO2 conditions that were much higher than current levels [87]. Hence, many plants flourish at higher CO2 concentrations [88] and warmer temperatures, so they might be expected to thrive in the future higher CO2 environment that has been predicted with continuing climate change. An eight-year study of a deciduous forest confirmed that carbon-enrichment increased net photosynthesis by 42\u201348% relative to controls [89]. Similarly, free-air enrichment by supplementary CO2 in field plots increased biomass yield by 15\u201327% in three poplar genotypes (P. alba, P. nigra, and Populus \u00d7 euramericana) [90].", "measurement_extractions": [ { "quantity": "eight-year", "unit": "year", "measured_entity": "study", "measured_property": null }, { "quantity": "42\u201348%", "unit": "%", "measured_entity": "deciduous forest", "measured_property": "increased net photosynthesis" }, { "quantity": "15\u201327%", "unit": "%", "measured_entity": "free-air enrichment by supplementary CO2 in field plots", "measured_property": "increased biomass yield" } ], "split": "train", "docId": "S0168945213001805-4454", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Improved photosynthesis increases sucrose production but this must be coupled with increased sucrose utilization, storage, or transport to sink tissues to avoid negative feedback regulation [102]. Increased downstream utilization of photosynthetic products has been shown to increase biomass yield [103]. Cellulose and hemicellulose account for \u223c70% of wood [104] so over-expression of genes that promote cellulose production should have a significant impact on timber production.", "measurement_extractions": [ { "quantity": "\u223c70%", "unit": "%", "measured_entity": "wood", "measured_property": "Cellulose and hemicellulose" } ], "split": "train", "docId": "S0168945213001805-4536", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Over-expression of master regulators for cold tolerance can produce cold-tolerant leaves and photosynthetic apparatus, including tolerance to photoinhibition in winter. Over-expression of two endogenous cold-binding factor/drought responsive element binding (CBF/DREB)1-like genes in rape (B. napus) increased freezing tolerance and improved photochemical efficiency and photosynthetic capacity. These transgenics were less affected by photoinhibition induced by low temperatures and high insolation [99]. Similarly, over-expression of two CBF/DREB genes from Eucalyptus gunnii, a species that can withstand winter temperatures down to \u221218 \u00b0C, improved drought and freezing tolerance of a cold-sensitive Eucalyptus hybrid (E. urophylla \u00d7 E. grandis) [136]. Tropical E. grandis \u00d7 E. urophylla expressing a stress-inducible rd29a promoter-CBF2 transcription factor cassette demonstrated stable tolerance to \u22128 \u00b0C in a variety of locations through multiple years with no significant loss in productivity [130].", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "endogenous cold-binding factor/drought responsive element binding (CBF/DREB)1-like genes", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "CBF/DREB genes", "measured_property": null }, { "quantity": "down to \u221218 \u00b0C", "unit": "\u00b0C", "measured_entity": "Eucalyptus gunnii", "measured_property": "withstand winter temperatures" }, { "quantity": "\u22128 \u00b0C", "unit": "\u00b0C", "measured_entity": "Tropical E. grandis \u00d7 E. urophylla expressing a stress-inducible rd29a promoter-CBF2 transcription factor cassette", "measured_property": "stable tolerance" } ], "split": "train", "docId": "S0168945213001805-4775", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Four-year-old wild-type cottonwood (P. trichocarpa) suffered significant defoliation at normal infestation levels of the leaf beetle (Chrysomela scripta). The wild-type trees had a net growth that was 13% less than those over-expressing the cry toxins from Bt. The trees producing the Cry3A toxin had very low feeding damage [165]. Eucalypts over-expressing a sequence-enhanced version of cry3Aa produced leaves that were toxic to three beetle species [166].", "measurement_extractions": [ { "quantity": "Four-year-old", "unit": "year-old", "measured_entity": "wild-type cottonwood", "measured_property": null }, { "quantity": "13%", "unit": "%", "measured_entity": "wild-type trees", "measured_property": "net growth" }, { "quantity": "three", "unit": null, "measured_entity": "beetle species", "measured_property": null } ], "split": "train", "docId": "S0168945213001805-5026", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "To increase reflectance of the HSQ film and to avoid light transmission and reflection from the metal substrate, we sputter coated cured HSQ samples with a thin film of gold/palladium (80/20) alloy. We used the Balzers SCD 004 sputter coater, operated with 0.06\u20130.07 mBar pressure of Argon. Samples have been processed for 240 s at 30 mA current. A silicon wafer is always included to examine the effect of sputtering on roughness.", "measurement_extractions": [ { "quantity": "(80/20", "unit": null, "measured_entity": "alloy", "measured_property": "gold/palladium" }, { "quantity": "0.06\u20130.07 mBar", "unit": "mBar", "measured_entity": "Argon", "measured_property": "pressure" }, { "quantity": "240 s", "unit": "s", "measured_entity": "Samples", "measured_property": "processed" }, { "quantity": "30 mA", "unit": "mA", "measured_entity": "processed", "measured_property": "current" } ], "split": "train", "docId": "S0169433213008933-689", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For late Quaternary records, most tephra horizons in sediment cores are dated by (linear) interpolation of 14C dates of carbonates, bulk sediment or organic remains. However, lakes are especially prone to a reservoir effect, due to dissolution of ancient carbonates and/or old organic matter that is washed into the lake from the catchment and incorporated into the sediment. Both phenomena can result in anomalous 14C ages and an offset from the real age of the dated material, which is not necessarily constant through time in the sediment section (Geyh et al., 1998; Yu et al., 2007). This offset from the real age can be of the order of several hundred or even more than a thousand years if bulk sediment or carbonates are dated, and can thus lead to misleading or erroneous age models if it is not taken into account (Geyh et al., 1998; Bertrand et al., 2012). Sediment sections spanning time intervals with multiple tephra layers could therefore easily be miscorrelated if the tephra is not adequately characterised to back up correlations. Appropriate tephra characterisation can potentially also help in evaluating the reservoir effect in a sediment section by providing a tie-point in the age model, provided the age of the tephra is well known from one or more independent sources (e.g., 14C dating on charcoal incorporated in terrestrial deposits, for example the well-constrained 3.00\u20133.05 ka cal BP Alpehu\u00e9 Pumice (So-A) eruption from Sollipulli; Supplementary Table 1; Naranjo et al., 1993b).", "measurement_extractions": [ { "quantity": "several hundred or even more than a thousand years", "unit": "years", "measured_entity": "real age", "measured_property": "offset from" }, { "quantity": "3.00\u20133.05 ka", "unit": "ka", "measured_entity": "cal BP Alpehu\u00e9 Pumice (So-A) eruption", "measured_property": null } ], "split": "train", "docId": "S027737911400050X-2401", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The water was ultrapure water obtained from a Milli-Q-system (Millipore Corporation, Bedford, MA, USA) and nitric acid (68 \u2013 70%), hydrochloric acid (30%), ammonium carbonate (powder), hydrogen peroxide (30%) and formic acid (98%) were all from J. T. Baker (Deventer, Netherlands). In the arsenic speciation analysis arsenobetaine (AB) (Fluka Analytical, Italy), arsenic(III)oxide (As(III)) (Aldrich Chemistry, USA), dimethyl arsenic acid (DMA) (Chem Service, USA), monomethyl arsenic acid disodium salt (MMA) (Argus Chemicals, Italy) and arsenic(V) (As(V)) standard solution (Merck, Germany) were used.", "measurement_extractions": [ { "quantity": "68 \u2013 70%", "unit": "%", "measured_entity": "nitric acid", "measured_property": null }, { "quantity": "30%", "unit": "%", "measured_entity": "hydrochloric acid", "measured_property": null }, { "quantity": "30%", "unit": "%", "measured_entity": "hydrogen peroxide", "measured_property": null }, { "quantity": "98%", "unit": "%", "measured_entity": "formic acid", "measured_property": null } ], "split": "train", "docId": "S030881461301604X-1001", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Two stock solutions of each standard compound were made; for AB, As(III), DMA and MMA the concentrations were 100 mg/L and 1 mg/L and for As(V) the concentrations were 10 mg/L and 0.1 mg/L. The stock solutions were prepared in nitric acid (1%), with the exception of As(III), in which concentrated hydrochloric acid was used to promote its dissolution. The final standard concentrations for all compounds were 1, 5, 10, 20 and 50 \u03bcg/L in 1% nitric acid.", "measurement_extractions": [ { "quantity": "Two", "unit": null, "measured_entity": "stock solutions", "measured_property": null }, { "quantity": "100 mg/L", "unit": "mg/L", "measured_entity": "DMA", "measured_property": "concentrations" }, { "quantity": "1 mg/L", "unit": "mg/L", "measured_entity": "MMA", "measured_property": "concentrations" }, { "quantity": "10 mg/L", "unit": "mg/L", "measured_entity": "DMA", "measured_property": "concentrations" }, { "quantity": "0.1 mg/L", "unit": "mg/L", "measured_entity": "MMA", "measured_property": "concentrations" }, { "quantity": "1%", "unit": "%", "measured_entity": "nitric acid", "measured_property": null }, { "quantity": "1, 5, 10, 20 and 50 \u03bcg/L", "unit": "\u03bcg/L", "measured_entity": "final standard concentrations", "measured_property": null }, { "quantity": "1%", "unit": "%", "measured_entity": "nitric acid", "measured_property": null } ], "split": "train", "docId": "S030881461301604X-1002", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In upland areas of Great Britain, large tracts of non-native conifer plantations have been established on poor quality agricultural land. There is now considerable interest in the conversion of some of these plantations to a more natural woodland comprised of native tree species. We studied the tree regeneration and ground flora on 15 upland sites (altitudes ranging from 120 m to 380 m above sea level) that had been clearfelled of conifers. Regeneration of native tree species was successful where a clearcut site was adjacent to mature native trees, which acted as a seed source. Mean regeneration densities of native tree species on clearcut sites were typically greater than 1000 stems/ha, exceeding minimum recommended planting densities for the establishment of new native woodland. Whilst 10 native woody tree species were recorded, the regeneration was dominated by birch species. Regeneration densities were significantly higher on clearcut sites than on adjacent areas of unplanted moorland, probably due to the lack of a dense ground flora following the clearfelling operations. Our results indicate that where local native seed sources exist, clearfelling upland conifer plantation sites to allow natural regeneration has the potential to be an effective method of establishing native woodland.", "measurement_extractions": [ { "quantity": "15", "unit": null, "measured_entity": "upland sites", "measured_property": null }, { "quantity": "120 m to 380 m", "unit": "m", "measured_entity": "upland sites", "measured_property": "altitudes" }, { "quantity": "greater than 1000 stems/ha", "unit": "stems/ha", "measured_entity": "native tree species", "measured_property": "regeneration densities" }, { "quantity": "10", "unit": null, "measured_entity": "native woody tree species", "measured_property": null } ], "split": "train", "docId": "S0378112713005288-1720", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Table 5 shows that the regeneration density of different site types (upland improved farmland or upland moorland). Site type (upland improved farmland or upland moorland) produced a significant variation in total regeneration densities (F(3, 8.9) = 4.1, p = 0.03). 20% of the total observed variation was due to variation between the different site types. The overall regeneration density on clearfelled upland moorland was significantly greater than on unplanted upland moorland (p < 0.01). However there was no significant difference between the regeneration density of clearfelled improved farmland and unplanted improved farmland (see Table 5). No significant difference in regeneration densities was found between brown earth and peaty gley soils (F(1, 3.95) = 1.75, p = n.s.).", "measurement_extractions": [ { "quantity": "4.1", "unit": null, "measured_entity": "significant variation", "measured_property": "F(3, 8.9)" }, { "quantity": "0.03", "unit": null, "measured_entity": "significant variation in total regeneration densities", "measured_property": "p" }, { "quantity": "20%", "unit": "%", "measured_entity": "total observed variation", "measured_property": "variation between the different site types" }, { "quantity": "< 0.01", "unit": null, "measured_entity": "significantly greater", "measured_property": "p" }, { "quantity": "1.75", "unit": null, "measured_entity": "No significant difference in regeneration densities", "measured_property": "F(1, 3.95)" }, { "quantity": "n.s.", "unit": null, "measured_entity": "No significant difference in regeneration densities", "measured_property": "p" } ], "split": "train", "docId": "S0378112713005288-1948", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Regeneration of oak and rowan was found to be significantly clumped although not significantly dependent on distance from the seed source. Rowan is primarily dispersed through ingestion by birds, particularly various thrush species (Raspe et al., 2000), while oak relies on hoarding by both birds and mammals but especially Garrulus glandarius (jay) and Apodemus sylvaticus (wood mouse) (Forget et al., 2004), both of which occur at the study sites. The distribution of regenerating saplings will therefore be partly controlled by the behaviour of the dispersing animal. Previous work in central Europe has demonstrated that the majority of oak regeneration occurs within 100 m of a seed source and declines rapidly at greater distances (Mirschel et al., 2011). However, our findings are in contrast to previous work carried out in lowland sites in the UK that found positive relationships between the number of oak seedlings and distance to parent trees but no significant effect for birch seedlings (Harmer et al., 2005), possibly indicating differences between the shelterwood examined by Harmer et al. (2005) and the more extensive clearfells that we considered.", "measurement_extractions": [ { "quantity": "within 100 m", "unit": "m", "measured_entity": "seed source", "measured_property": "majority of oak regeneration" } ], "split": "train", "docId": "S0378112713005288-2036", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We found that the dense layers of brash produced by windrowing significantly reduced the amount of natural regeneration. Windrows could be up to a metre high and several metres wide, producing a physical barrier that prevented seedling establishment and creating regions with little or no regeneration. While we might expect seedlings from larger seeded species like rowan (200,000 seeds weigh 1 kg) to have an advantage over seedlings from smaller seeded species such as birch (5.9 million seeds weigh 1 kg) in growing through brash (Leishman and Westoby, 1994) we found no significant difference between the proportion of rowan in windrows and interrows. Furthermore, previous studies have found that where grazing pressure is high, brash (Truscott et al., 2004) and coarse woody debris (Smit et al., 2012) can help protect seedlings from browsing. However, it is difficult to draw any conclusions from our study as only a single site (U15) recorded significant browsing. The low incidence of browsing at our study sites (grazing pressure was controlled) means that grazing is unlikely to limit regeneration (Palmer et al., 2004; Olesen and Madsen, 2008; Yamagawa et al., 2010).", "measurement_extractions": [ { "quantity": "200,000", "unit": null, "measured_entity": "seeds", "measured_property": null }, { "quantity": "1 kg", "unit": "kg", "measured_entity": "200,000 seeds", "measured_property": "weigh" }, { "quantity": "5.9 million", "unit": null, "measured_entity": "seeds", "measured_property": null }, { "quantity": "1 kg", "unit": "kg", "measured_entity": "5.9 million seeds", "measured_property": "weigh" } ], "split": "train", "docId": "S0378112713005288-2062", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Depth time-series and depth-averaged bed-parallel velocity time-series at x = 0.072 m for the 1.3 mm sand-rough beach; results from 50 individual events (grey) and ensemble-averaged result (black).", "measurement_extractions": [ { "quantity": "0.072 m", "unit": "m", "measured_entity": "Depth time-series and depth-averaged bed-parallel velocity time-series", "measured_property": "x" }, { "quantity": "1.3 mm", "unit": "mm", "measured_entity": "sand-rough beach", "measured_property": null }, { "quantity": "50", "unit": null, "measured_entity": "individual events", "measured_property": null } ], "split": "train", "docId": "S0378383911001669-1088", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Depth time-series at all six measurement locations for the 1.3 mm sand-rough (-), the 5.4 mm gravel-rough (--) and the 8.4 mm gravel-rough (-.-.) beaches.", "measurement_extractions": [ { "quantity": "six", "unit": null, "measured_entity": "measurement locations", "measured_property": null }, { "quantity": "1.3 mm", "unit": "mm", "measured_entity": "beaches", "measured_property": null }, { "quantity": "5.4 mm", "unit": "mm", "measured_entity": "beaches", "measured_property": null }, { "quantity": "8.4 mm", "unit": "mm", "measured_entity": "beaches", "measured_property": null } ], "split": "train", "docId": "S0378383911001669-1112", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Flow depths were measured using Laser-induced fluorescence (LIF) (Sue et al., 2006). Fluorescent dye was added to the water, with a concentration of approximately 0.1 mg/l and illuminated by the Nd-YAG Laser. The emitted light from the fluorescent dye was recorded by a Kodak Megaplus ES1.0 b/w digital video camera fitted with a 50 mm fixed focal length lens and a Hasselblad orange filter. The camera was aligned with the 1:10 slope of the beach and rotated forwards so that the camera view was at all times above the free surface (Fig. 4).", "measurement_extractions": [ { "quantity": "approximately 0.1 mg/l", "unit": "mg/l", "measured_entity": "water", "measured_property": "Fluorescent dye" }, { "quantity": "50 mm", "unit": "mm", "measured_entity": "lens", "measured_property": "fixed focal length" }, { "quantity": "1:10", "unit": null, "measured_entity": "beach", "measured_property": "slope" } ], "split": "train", "docId": "S0378383911001669-1634", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The magnitudes of the friction factors for the two beaches are very similar over the majority of the swash cycle. This may be somewhat surprising since one would expect higher friction factors for the rougher beach. (For reference, friction factors calculated assuming steady and uniform flow and evaluated from the measured depths and velocities, with equivalent roughness of 1.3 mm and 8.4 mm, are consistently higher for the rougher beach by approximately 50%.) The fact that the friction factors are not very different in the present experiments suggests that the high flow unsteadiness and non-uniformity of swash have significant influence on the bed shear stress for a given instantaneous velocity. The boundary layer development is likely to play a significant role and its effect can be observed in the results during the uprush. At x = 0.072 m the boundary layer has developed to only a limited extent, so the friction factors for the two beaches are very close. Further up the slope, beyond x = 2.377 m, the friction factors are higher for the coarser 8.4 mm beach. The increase in friction factor as flow reversal is approached is consistent with cf behaviour in uniform, steady flow in that cf is increasing with decreasing Reynolds number and higher relative roughness. In contrast, cf behaviour during the early stage of the backwash is different from cf behaviour in uniform, steady flow in that cf is initially low, despite the Reynolds number being low and the relative roughness being high. Somewhat later, cf values for the 8.4 mm beach catch up on the values for the 1.3 mm beach and then become higher than the 1.3 mm beach values, but only for locations higher up the beach. At the two lowest locations on the beach cf values are surprisingly similar during the backwash. This means that the boundary layer development is not sufficient to explain cf behaviour throughout the whole swash cycle.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "beaches", "measured_property": null }, { "quantity": "1.3 mm and 8.4 mm,", "unit": "mm", "measured_entity": "two beaches", "measured_property": "roughness" }, { "quantity": "approximately 50%", "unit": "%", "measured_entity": "rougher beach", "measured_property": "friction factors" }, { "quantity": "0.072 m", "unit": "m", "measured_entity": "x", "measured_property": null }, { "quantity": "2.377 m", "unit": "m", "measured_entity": "x", "measured_property": null }, { "quantity": "8.4 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "8.4 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "1.3 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "1.3 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "lowest locations on the beach", "measured_property": null } ], "split": "train", "docId": "S0378383911001669-2260", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Surface and subsurface flow profiles as predicted by the model (shaded area) and measured in experiments (dark lines). Results for the 1.5 mm beach.", "measurement_extractions": [ { "quantity": "1.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null } ], "split": "train", "docId": "S0378383912000130-1048", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Surface and subsurface flow profiles as predicted by the model (shaded area) and measured in experiments (dark lines). Results for the 8.5 mm beach.", "measurement_extractions": [ { "quantity": "8.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null } ], "split": "train", "docId": "S0378383912000130-1054", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Time-series of model-predicted (solid lines) and experimentally-measured (dashed lines) hydraulic head, H\u03b7, within the 8.5 mm beach at x = 1180 and 1980 mm.", "measurement_extractions": [ { "quantity": "8.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "1180 and 1980 mm", "unit": "mm", "measured_entity": "Time-series of model-predicted (solid lines) and experimentally-measured (dashed lines) hydraulic head", "measured_property": "x" } ], "split": "train", "docId": "S0378383912000130-1096", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The numerical model described in Section 2 is used to simulate the large-scale laboratory experiments presented in Steenhauer et al. (2011a). Fig. 5 illustrates the set-up in a Cartesian system with x and z coordinates in the horizontal and vertical directions respectively, and with the origin at the initial shoreline position. The experiments involved two beach materials with nominal sediment diameters of 1.5 mm and 8.5 mm. The dam-break set-up is simulated through a water reservoir with initial water depth in the reservoir 600 mm for experimental series R60PER015 of the 1.5 mm beach and experimental series R60PER085 of the 8.5 mm beach. An initial water level of 62 mm in front of the beach and a corresponding groundwater level of 62 mm within the beach were used in both cases. The initial shoreline position is defined through the initial water depth in front of the reservoir. The slope of the beach was 1:10. A detailed description of the experimental set-up, surface and subsurface measurements and results are presented in Steenhauer et al. (2011a).", "measurement_extractions": [ { "quantity": "two beach materials", "unit": "beach materials", "measured_entity": "experiments", "measured_property": "involved" }, { "quantity": "1.5 mm and 8.5 mm", "unit": "mm", "measured_entity": "beach materials", "measured_property": "nominal sediment diameters" }, { "quantity": "600 mm", "unit": "mm", "measured_entity": "reservoir", "measured_property": "initial water depth" }, { "quantity": "1.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "8.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "62 mm", "unit": "mm", "measured_entity": "water", "measured_property": "level" }, { "quantity": "62 mm", "unit": "mm", "measured_entity": "groundwater", "measured_property": "level" }, { "quantity": "1:10", "unit": null, "measured_entity": "beach", "measured_property": "slope" } ], "split": "train", "docId": "S0378383912000130-3601", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The groundwater module was used only for the 8.5 mm beach, where the groundwater response was directly observed at bore arrival. In the 1.5 mm beach the groundwater levels were not affected during the swash event, because the wetting front did not reach the groundwater within the swash cycle, due to the low permeability of the beach. This means that the simulation for the 1.5 mm beach is carried out with a constant groundwater level equal to the initial groundwater level. All other modules were used for both beaches.", "measurement_extractions": [ { "quantity": "8.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "1.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "1.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null } ], "split": "train", "docId": "S0378383912000130-3662", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Vertical hydraulic gradients governing the rates of infiltration and exfiltration assess the exchange of water between the surface and the subsurface. Fig. 9 presents time-series of the hydraulic gradient at several cross-shore locations for the 1.5 mm and the 8.5 mm beaches. Note that the results are given relative to time t0, which is the bore arrival time at each cross-shore location, and only while the wetting front is still moving towards the groundwater table. Positive gradients are associated with infiltration.", "measurement_extractions": [ { "quantity": "1.5 mm and the 8.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null } ], "split": "train", "docId": "S0378383912000130-3732", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For the 8.5 mm beach the modelling produces a reasonably accurate trend at x = 1980 mm, while at x = 2780 mm the model fails to predict the initial sharp rise and the decline at the later stages of the swash. In this case the discrepancy between experimental and numerical results is probably caused by the modelling, most likely by the limited capability of the groundwater module.", "measurement_extractions": [ { "quantity": "8.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "1980 mm", "unit": "mm", "measured_entity": "modelling", "measured_property": "x" }, { "quantity": "2780 mm", "unit": "mm", "measured_entity": "modelling", "measured_property": "x" } ], "split": "train", "docId": "S0378383912000130-3745", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Overall the time-series of the hydraulic gradients display the steep increase at the time of bore arrival. For both beaches hydraulic gradients gradually decrease with time, as the saturated zone above the wetting front becomes thicker. Moreover, hydraulic gradients are significantly reduced by the build-up of pore-air pressure in the unsaturated region of the 1.5 mm beach (Steenhauer et al., 2011a). This pressure build-up is discussed in Section 3.3.", "measurement_extractions": [ { "quantity": "1.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null } ], "split": "train", "docId": "S0378383912000130-3755", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In contrast to the 1.5 mm beach, within the 8.5 mm beach infiltration was rapid, and the wetting front reached the groundwater level across the majority of the swash zone during the uprush. Groundwater response was hence simulated only in the case of the 8.5 mm beach and discussed in the following.", "measurement_extractions": [ { "quantity": "1.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "8.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "8.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null } ], "split": "train", "docId": "S0378383912000130-3827", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The numerical study shows that when the upwards-driven wetting front has reached the level of the bed surface of the 1.5 mm beach, a pathway is created to release the air, at a higher pressure than atmospheric pressure, entrapped within the beach. Entrapped air is then not only released through the unsaturated region of the beach beyond the shoreline position, but also through the additional flow paths created at the lower end of the beach, where the beach has returned to its initially unsaturated state.", "measurement_extractions": [ { "quantity": "1.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null } ], "split": "train", "docId": "S0378383912000130-3907", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Power spectra of surface elevation for a JONSWAP spectrum with fp = 0.5 Hz and Hp = 1.8 cm: comparison of the numerical results (solid line) with experimental data (dashed line).", "measurement_extractions": [ { "quantity": "0.5 Hz", "unit": "Hz", "measured_entity": "JONSWAP spectrum", "measured_property": "fp" }, { "quantity": "1.8 cm", "unit": "cm", "measured_entity": "JONSWAP spectrum", "measured_property": "Hp" } ], "split": "train", "docId": "S0378383913001567-2462", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Three different methods were used to recover sediment cores in 2010. The upper 0.8 m of sediments were recovered using a freeze-coring technique (Renberg and Hansson, 1993). This core recovers a thin (approximately 1 cm) vertical slab of relatively undisturbed sediment and was only used for correlation purposes. The freezing technique physically disturbs the micro-structure of unconsolidated sediments and paleomagnetic sub-samples were not obtained from it. Five cores for paleomagnetic measurements were recovered using a rod-operated fixed-piston corer described by Snowball and Sandgren (2002). This system recovers complete 4.8 m long cores of sediment in PVC tubes of either 60 or 63 mm internal diameter, although it is not oriented to a geographic azimuth for practical reasons. This system provided four overlapping cores for paleomagnetic reconstructions to a sediment depth of 8 m (GD0a, GP1, GP2 and GP4. GP3 failed due to an insecure piston). A cable operated Uwitec \u201cNiederreiter\u201d percussion piston corer (three metre long drives) was subsequently used to recover sediments to depth of approximately 11.8 m (drives GD1\u2013GD4). Sandy sediment was recovered in the bottom of GD4, equivalent to a sediment depth between 11.8 m and 11.5 m and it proved impossible to penetrate through this material with the available equipment.", "measurement_extractions": [ { "quantity": "upper 0.8 m", "unit": "m", "measured_entity": "sediments", "measured_property": null }, { "quantity": "approximately 1 cm", "unit": "cm", "measured_entity": "vertical slab of relatively undisturbed sediment", "measured_property": "thin" }, { "quantity": "Five", "unit": null, "measured_entity": "cores", "measured_property": null }, { "quantity": "4.8 m", "unit": "m", "measured_entity": "cores of sediment", "measured_property": "long" }, { "quantity": "60 or 63 mm", "unit": "mm", "measured_entity": "PVC tubes", "measured_property": "internal diameter" }, { "quantity": "four", "unit": null, "measured_entity": "overlapping cores", "measured_property": null }, { "quantity": "8 m", "unit": "m", "measured_entity": "sediment", "measured_property": "depth" }, { "quantity": "three metre", "unit": "metre", "measured_entity": "drives", "measured_property": "long" }, { "quantity": "approximately 11.8 m", "unit": "m", "measured_entity": "sediments", "measured_property": "depth" }, { "quantity": "between 11.8 m and 11.5 m", "unit": "m", "measured_entity": "sediment", "measured_property": "depth" } ], "split": "train", "docId": "S0921818113002245-1571", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In general the sediment units (fine detritus gyttja) become lighter coloured towards the surface of the sequence. There are, however, distinct changes in magnetic susceptibility within GU-3, which also contains a distinct layer of clastic sediment (approximately 1 mm thick) with high magnetic susceptibility at depth of 5.16 m. Magnetic susceptibility remains variable in GU-2, which is a lighter brown fine detritus gyttja. The onset of liming within the catchment causes GU-1 to be characterised by thin layers of light coloured non-dissolved calcium carbonate. The magnetic susceptibility of the freeze-core was not determined.", "measurement_extractions": [ { "quantity": "approximately 1 mm", "unit": "mm", "measured_entity": "distinct layer of clastic sediment", "measured_property": "thick" }, { "quantity": "5.16 m.", "unit": "m", "measured_entity": "distinct layer of clastic sediment", "measured_property": "depth" } ], "split": "train", "docId": "S0921818113002245-1752", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Site location. The inset shows an outline map of Sweden and the approximate location of Gyltigesj\u00f6n (circle) in the south. The coring location at the lakes deepest point of approximately 19 m is marked by the white circle. Large (small) arrows show major (minor) inflows and outflows. The bathymetry is based on Guhr\u00e9n et al. (2003).", "measurement_extractions": [ { "quantity": "approximately 19 m", "unit": "m", "measured_entity": "lakes", "measured_property": "deepest point" } ], "split": "train", "docId": "S0921818113002245-859", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Age\u2013depth relationship and physical properties. The solid line in (a) shows the OxCal modelled age\u2013depth relationship produced by integrating the radiocarbon wiggle-match of a series of 873 varves (Mellstr\u00f6m et al., 2013) with a series of terrestrial macrofossil ages (labels 46\u201352.1.1). The dashed back line shows the approximate depths of an historically dated atmospheric lead isochron established in a nearby core by Guhr\u00e9n et al. (2003). The sedimentation rate shown in (b) was calculated by interpolation between radiocarbon-dated levels, the AD 1850 lead pollution horizon (Guhr\u00e9n et al., 2003) and the sediment surface. (c) Loss-on-ignition, which is relatively stable in the sediments above 10.5 m depth. The wet density of the discrete paleomagnetic samples is shown in (d).", "measurement_extractions": [ { "quantity": "873 varves", "unit": "varves", "measured_entity": "series", "measured_property": null }, { "quantity": "AD 1850", "unit": null, "measured_entity": "lead pollution horizon", "measured_property": null }, { "quantity": "above 10.5 m", "unit": "m", "measured_entity": "sediments", "measured_property": "depth" } ], "split": "train", "docId": "S0921818113002245-882", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Human skin fibroblasts were cultured in DMEM medium (Dulbecco's modified Eagle's medium, Gibco) supplemented with 10% (v/v) fetal calf serum (FCS), 2 mM l-glutamine, 50 \u03bcg/ml uridine, 110 \u03bcg/ml pyruvate, 10,000 U/ml penicillin G and 10,000 \u03bcg/ml streptomycin.", "measurement_extractions": [ { "quantity": "10%", "unit": null, "measured_entity": "DMEM medium", "measured_property": "fetal calf serum (FCS)" }, { "quantity": "2 mM", "unit": "mM", "measured_entity": "DMEM medium", "measured_property": "l-glutamine" }, { "quantity": "50 \u03bcg/ml", "unit": "\u03bcg/ml", "measured_entity": "DMEM medium", "measured_property": null }, { "quantity": "110 \u03bcg/ml", "unit": "\u03bcg/ml", "measured_entity": "DMEM medium", "measured_property": "pyruvate" }, { "quantity": "10,000 U/ml", "unit": "U/ml", "measured_entity": "DMEM medium", "measured_property": "penicillin G" }, { "quantity": "10,000 \u03bcg/ml", "unit": "\u03bcg/ml", "measured_entity": "DMEM medium", "measured_property": "streptomycin" } ], "split": "train", "docId": "S0925443913001385-1429", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The steady state level of MRPL12 in the subject's fibroblasts was reduced to 30% of control value (Fig. 4A and B). The mt-LSU protein ICT1 was also decreased (~ 30% of control values) as was MRPL3 (by 37%) suggesting that a consequence of the MRPL12 mutation is a global defect in assembly of the large ribosomal subunit (mt-LSU). In order to estimate the effect of the MRPL12 mutation on assembly of the whole ribosome, we also tested three proteins of the small ribosomal subunit (SSU), MRPS18B, MRPS25 and DAP3. These were modestly decreased with levels of ~ 60\u201380% of control (Fig. 4A and B). Correspondingly, 16S and 12S rRNA levels were decreased by 35% and 22% respectively (Fig. 7A). Since porin indicated that there was no compensatory mitochondrial biogenesis and staining of the mitochondrial network with tetramethylrhodamine methyl ester showed no significant alteration in amount or distribution of mitochondria (AR and ZCL unpublished observation) we conclude that the MRPL12 mutation destabilizes the protein resulting in less mt-LSU and to a lesser extent of the small subunit.", "measurement_extractions": [ { "quantity": "30%", "unit": "%", "measured_entity": "control value", "measured_property": "steady state level of MRPL12 in the subject's fibroblasts" }, { "quantity": "~ 30%", "unit": "%", "measured_entity": "control values", "measured_property": "mt-LSU protein ICT1" }, { "quantity": "37%", "unit": "%", "measured_entity": "control values", "measured_property": "MRPL3" }, { "quantity": "three", "unit": null, "measured_entity": "proteins of the small ribosomal subunit (SSU)", "measured_property": null }, { "quantity": "~ 60\u201380%", "unit": "%", "measured_entity": "control", "measured_property": "MRPS18B, MRPS25 and DAP3" }, { "quantity": "35%", "unit": "%", "measured_entity": "control", "measured_property": "16S" }, { "quantity": "22%", "unit": "%", "measured_entity": "control", "measured_property": "12S rRNA levels" } ], "split": "train", "docId": "S0925443913001385-1638", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In order to determine whether the MRPL12 mutation also induced changes in composition and assembly of the mitochondrial ribosomal large and small subunits, mitochondrial lysates from cultured fibroblasts (subject and control) were fractionated on isokinetic sucrose gradients (10\u201330%, as in Ref. [47]). If assembly of either the large subunit or the entire ribosome was affected then the distribution of individual ribosomal proteins would change within the gradient profile. On analysis MRPL12 from the patient was substantially decreased in all fractions but detectable in the fractions consistent with mt-LSU; however it was noticeably absent from the free pool (fractions 1 and 2, Fig. 5). This was in contrast to the control that exhibited a pool of free MRPL12, which has been reported to interact with POLRMT [56]. MRPL3 was also slightly reduced in subject cells but remained in fractions consistent with the large subunit. The MRPL12 mutation impacted more modestly on the small ribosomal subunit, with DAP3 apparently unaffected and MRPS18B found in lower amounts only in fractions 4 and 5 but otherwise with similar steady state levels and distribution profile compared to control. Since POLRMT and MRPL12 have been published as interactors, we analyzed both the steady state level and gradient distribution of POLRMT to see if these were affected by the MRPL12 mutation. Overall levels in the subject sample were decreased to 63% of control value (Fig. 5B) but distribution in the gradient appeared largely unaffected with the exception of fraction 11, where levels were lower than control (Fig. 5A bottom panels).", "measurement_extractions": [ { "quantity": "10\u201330%", "unit": "%", "measured_entity": "isokinetic sucrose gradients", "measured_property": null }, { "quantity": "63%", "unit": "%", "measured_entity": "control value", "measured_property": "levels in the subject sample" } ], "split": "train", "docId": "S0925443913001385-1646", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Mitochondrial protein synthesis. De novo synthesis of mitochondrial proteins was determined in patient (P) and control (C) fibroblasts under conditions that inhibited cytosolic translation [45]. In vivo incorporation of 35S-methionine/cysteine into mitochondrially encoded proteins was visualised by separation of cell lysate (50 \u03bcg) through SDS\u2013PAGE, exposure of the dried gel to a PhosphorImage screen, followed by Storm and ImageQuant analysis (upper panel). To the right of the gel are the aligned densitometric profiles of the patient (lower trace) and control (upper trace). The gel was subsequently rehydrated and stained with Coomassie blue to confirm equal loading (lower panel).", "measurement_extractions": [ { "quantity": "50 \u03bcg", "unit": "\u03bcg", "measured_entity": "cell lysate", "measured_property": null } ], "split": "train", "docId": "S0925443913001385-839", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "To study the effect of the FARS2 mutations on the steady state levels of mt-tRNAPhe, high resolution northern blotting was performed using total RNA extracted from patient myoblasts. The levels of mt-tRNAVal and mt-tRNALeu(UUR) were used as loading controls and the level of mt-tRNAPhe was assessed as a percentage relative to controls. We observed an approximately 54% reduction in the level of mt-tRNAPhe when compared with normal controls (Fig. 3D).", "measurement_extractions": [ { "quantity": "approximately 54%", "unit": "%", "measured_entity": "mt-tRNAPhe", "measured_property": "level" } ], "split": "train", "docId": "S0925443913003037-1397", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Cranial MRI performed at age 2.5 years.", "measurement_extractions": [ { "quantity": "2.5 years", "unit": "years", "measured_entity": "Cranial MRI performed", "measured_property": "age" } ], "split": "train", "docId": "S0925443913003037-654", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We have shown that inverted BHJ devices made with rapidly grown ZnO hole blocking layers using atmospheric atomic layer deposition (AALD) give device performances that are comparable with the best inverted P3HT:PCBM BHJ devices reported to date. Working devices with good, but lower performance were obtained without any post-annealing of the as-grown AALD ZnO. On the other hand, these unannealed AALD ZnO films are suitable for making flexible, plastic substrate solar cells. A compromise between device performance and suitability for making plastic substrate solar cells was achieved by post-annealing the ZnO films at 150 \u00b0C. The AALD ZnO fulfils the properties required for good hole blocking layers: the films are compact, have a high electron mobility, have up to 100% visible light transmittance, good blend wettability and very good device stability over time. In addition, the deposition process occurs under atmospheric conditions and has the potential to be implemented in a roll-to-roll process. AALD ZnO films are therefore highly suited as multifunctional components for inverted BHJ devices.", "measurement_extractions": [ { "quantity": "150 \u00b0C", "unit": "\u00b0C", "measured_entity": "ZnO films", "measured_property": "post-annealing" }, { "quantity": "up to 100%", "unit": "%", "measured_entity": "films", "measured_property": "visible light transmittance" } ], "split": "train", "docId": "S0927024813001955-1005", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Comparison of the performance of ITO/AALD ZnO/P3HT:PCBM/MoO3/Ag devices with the 125 nm thick AALD ZnO film post-annealed at different temperatures for 1 h. The uncertainties represent the variation in the properties among multiple devices made with the same parameters. The devices were measured after one week of storage in the dark under ambient conditions.", "measurement_extractions": [ { "quantity": "125 nm", "unit": "nm", "measured_entity": "AALD ZnO film", "measured_property": "thick" }, { "quantity": "1 h", "unit": "h", "measured_entity": "AALD ZnO film", "measured_property": "post-annealed" }, { "quantity": "one week", "unit": "week", "measured_entity": "devices", "measured_property": "storage in the dark" } ], "split": "train", "docId": "S0927024813001955-576", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A thin ZnO (<200 nm) film grown by Atmospheric Atomic Layer Deposition (AALD) in a matter of minutes was studied as a hole-blocking layer in poly(3-hexylthiophene-2,5-diyl):[6,6]-phenyl-C61-buyric acid methyl ester (P3HT:PCBM) based inverted solar cells. These AALD ZnO layers were compact, had a high electron mobility of 3.4+0.1 cm2/Vs, had up to 100% transmittance to visible light, and a good wettability for the blend. Despite the very rapid, open atmosphere growth method, the cell performance was comparable with some of the best inverted bulk heterojunction P3HT:PCBM cells in the literature. The performance was also maintained after 200 days of storage in air in the dark.", "measurement_extractions": [ { "quantity": "<200 nm", "unit": "nm", "measured_entity": "film", "measured_property": "thin" }, { "quantity": "3.4+0.1 cm2/Vs", "unit": "cm2/Vs", "measured_entity": "AALD ZnO layers", "measured_property": "electron mobility" }, { "quantity": "up to 100%", "unit": "%", "measured_entity": "AALD ZnO layers", "measured_property": "transmittance to visible light" }, { "quantity": "after 200 days", "unit": "days", "measured_entity": "performance", "measured_property": "maintained" } ], "split": "train", "docId": "S0927024813001955-679", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The performance and morphology of vacuum co-deposited BHJs was studied through variation of four main parameters\u2014active layer thickness, polymer\u2013fullerene composition and post-production thermal annealing temperature and time. In order to see the effect of annealing on different PTh:C60 compositions, optimization of the annealing temperature and time had to be performed. A series of initial experiments indicated that BHJ with 80 vol% of C60 and a thickness of 70 nm lead to device characteristics comparable to the previously reported planar heterojunction devices (Jsc\u223c2\u20133 mA/cm2 and Voc\u223c0.40\u20130.45 V) [37]. This composition thus served as a good standard for optimization of the annealing parameters.", "measurement_extractions": [ { "quantity": "80 vol%", "unit": "vol%", "measured_entity": "BHJ", "measured_property": "C60" }, { "quantity": "70 nm", "unit": "nm", "measured_entity": "BHJ", "measured_property": "thickness" }, { "quantity": "\u223c2\u20133 mA/cm2", "unit": "mA/cm2", "measured_entity": "planar heterojunction devices", "measured_property": "Jsc" }, { "quantity": "\u223c0.40\u20130.45 V", "unit": "V", "measured_entity": "planar heterojunction devices", "measured_property": "Voc" } ], "split": "train", "docId": "S0927024813002420-1032", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Vacuum-deposited PTh:C60 bulk heterojunctions with different donor\u2013acceptor compositions were fabricated and the effect of post-production thermal annealing on their photovoltaic performance and morphology studied. We showed that co-deposition of blended mixtures leads to 60% higher photocurrents than in thickness-optimized PTh/C60 planar heterojunction counterparts [37]. Furthermore, by annealing the devices post-situ we improved their power conversion efficiency by as much as 80%, achieving performance comparable to PTh:PCBM equivalents processed in solution from thermo-cleavable precursors [52]. An enhanced photoresponse results from the favorable morphological development of PTh upon annealing, modestly larger grain sizes and increased crystallization of C60. In contrast to most small-molecule blends, annealing-induced phase separation does not lead to formation of microcrystals but rather to a controlled improvement of the donor\u2013acceptor network. This is an inherent advantage of polymers over molecular systems.", "measurement_extractions": [ { "quantity": "60% higher", "unit": null, "measured_entity": "co-deposition of blended mixtures", "measured_property": "photocurrents" }, { "quantity": "as much as 80%", "unit": null, "measured_entity": "devices", "measured_property": "power conversion efficiency" } ], "split": "train", "docId": "S0927024813002420-1202", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Polythiophene (PTh) was purchased from Sigma-Aldrich (unknown Mw), poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS, Baytron P dispersion) from H.C. Starck and fullerene (C60, 99.9% pure) from MER Corporation. All the materials were used as received. PTh and C60 were deposited by vacuum thermal evaporation from separate tungsten boats (Leybold Optics) which were heated by a Xantrex XHR 7.5\u201380 DC Power Supply and TDK-Lambda Genesys 8\u2013300 DC Power Supply, respectively. The evaporation was conducted in high vacuum \u223c1\u00d710\u22125 Torr at a temperature of 300\u00b115 \u00b0C. The individual rate of PTh and C60 deposition was \u223c0.5\u20135 \u00c5/s depending on the BHJ composition. The average overall rate was approximately \u223c1.5\u20133 \u00c5/s. Due to the insolubility of PTh, no accurate direct measurement of the molecular weight could be made (nor was this value indicated by the manufacturer), however a combination of different techniques in our previous work [37] suggested that the evaporated PTh is near its effective conjugation length (20\u201325 monomer units, Eg\u22482.0 eV), which corresponds to Mw~1500\u20132000 g mol\u22121.", "measurement_extractions": [ { "quantity": "99.9%", "unit": "%", "measured_entity": "C60", "measured_property": "pure" }, { "quantity": "7.5\u201380", "unit": null, "measured_entity": "DC Power Supply", "measured_property": null }, { "quantity": "8\u2013300", "unit": null, "measured_entity": "DC Power Supply", "measured_property": null }, { "quantity": "\u223c1\u00d710\u22125 Torr", "unit": "Torr", "measured_entity": "evaporation", "measured_property": "vacuum" }, { "quantity": "300\u00b115 \u00b0C", "unit": "\u00b0C", "measured_entity": "evaporation", "measured_property": "temperature" }, { "quantity": "\u223c0.5\u20135 \u00c5/s", "unit": "\u00c5/s", "measured_entity": "PTh and C60 deposition", "measured_property": "rate" }, { "quantity": "\u223c1.5\u20133 \u00c5/s", "unit": "\u00c5/s", "measured_entity": "PTh and C60 deposition", "measured_property": "average overall rate" }, { "quantity": "20\u201325 monomer units", "unit": "monomer units", "measured_entity": "PTh", "measured_property": "effective conjugation length" }, { "quantity": "\u22482.0 eV", "unit": "eV", "measured_entity": "PTh", "measured_property": "Eg" }, { "quantity": "~1500\u20132000 g mol\u22121", "unit": "g mol\u22121", "measured_entity": "PTh", "measured_property": "Mw" } ], "split": "train", "docId": "S0927024813002420-975", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The energy yield delivered by different types of photovoltaic device is a key consideration in the selection of appropriate technologies for cheap photovoltaic electricity. The different technologies currently on the market, each have certain strengths and weaknesses when it comes to operating in different environments. There is a plethora of comparative tests on-going with sometimes contradictory results. This paper investigates device behaviour of contrasting thin film technologies, specifically a-Si and CIGS derivatives, and places this analysis into context with results reported by others. Specific consideration is given to the accuracy of module inter-comparisons, as most outdoor monitoring at this scale is conducted to compare devices against one another. It is shown that there are five main contributors to differences in energy delivery and the magnitude of these depends on the environments in which the devices are operated. The paper shows that two effects, typically not considered in inter-comparisons, dominate the reported energy delivery. Environmental influences such as light intensity, spectrum and operating temperature introduce performance variations typically in the range of 2\u20137% in the course of a year. However, most comparative tests are carried out only for short periods of time, in the order of months. Here, the power rating is a key factor and adds uncertainty for new technologies such as thin films often in the range of 10\u201315%. This dominates inter-comparisons looking at as-new, first-year energy yields, yet considering the life-time energy yield it is found that ageing causes up to 25% variation between different devices. The durability of devices and performance-maintenance is thus the most significant factor affecting energy delivery, a major determinant of electricity cost. The discussion is based on long-term measurements carried out in Loughborough, UK by the Centre for Renewable Energy Systems Technology (CREST) at Loughborough University.", "measurement_extractions": [ { "quantity": "five", "unit": null, "measured_entity": "main contributors", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "effects", "measured_property": null }, { "quantity": "2\u20137%", "unit": "%", "measured_entity": "performance variations", "measured_property": null }, { "quantity": "range of 10\u201315%", "unit": "%", "measured_entity": "performance variations", "measured_property": null }, { "quantity": "up to 25%", "unit": "%", "measured_entity": "different devices", "measured_property": "variation" } ], "split": "train", "docId": "S0927024813002961-1051", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "There are several time-scales involved in performance variations of photovoltaic devices. In the short term, there is the direct influence of the environment as discussed in Section 4. In the mid-term there is the Staebler\u2013Wronski effect for a-Si [48,49] or pre-conditioning for CIGS, which affects the annual energy prediction by less than 5% typically and are not further explored here. The effect for CIGS is slightly contradictory as there is no clarity of the time scales involved yet [4,50]. There are also long-term gradual degradation effects which affect all device technologies [51]. In many published discussions there are generalisations of technologies. It is shown below that such a general behaviour was not observed at CREST. The maximum power point data of all devices within an irradiance range of 650\u2013750 W/m2 have been extracted and corrected to 700 W/m2 and 25 \u00b0C using bi-linear interpolation. These power values are annotated as P700. This particular irradiance level was chosen as there is a statistically significant number of data points in each month of the year and thus fewer outliers to affect the analysis. The result is shown in Fig. 9 (top graph). To contrast power and energy, the monthly performance ratios are also plotted (bottom graph)Table 2.", "measurement_extractions": [ { "quantity": "less than 5%", "unit": "%", "measured_entity": "annual energy prediction", "measured_property": null }, { "quantity": "650\u2013750 W/m2", "unit": "W/m2", "measured_entity": "devices", "measured_property": "irradiance range" }, { "quantity": "700 W/m2", "unit": "W/m2", "measured_entity": "devices", "measured_property": "irradiance" }, { "quantity": "25 \u00b0C", "unit": "\u00b0C", "measured_entity": "devices", "measured_property": null } ], "split": "train", "docId": "S0927024813002961-1334", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Minority carrier lifetime was measured using transient or quasi-steady-state photoconductance [38] methods, with a Sinton WCT-120 lifetime tester. The injection level range studied varied with the lifetime of the sample, but was usually in the range 1013 cm\u22123 to 1016 cm\u22123. It is our aim to determine the absolute lifetime associated with oxygen precipitation, so care was taken to prevent or factor out well-understood recombination processes. Boron\u2013oxygen defects were eliminated by storing the samples in the dark after passivation, or by performing a 10 min pre-anneal at 200 \u00b0C [39] prior to lifetime measurement. The samples were subjected to \u223c50 close-up flashes from the lifetime tester to dissociate FeB pairs [40], after which an initial lifetime measurement was made immediately. It is noted that the aggregated illumination time of the flashes of light used to dissociate the iron\u2013boron pairs is very short (<20 ms), so any effect on the formation of boron\u2013oxygen defects is kept to a minimum [39]. A second lifetime measurement was made more than 24 h later, which was sufficient time to reassociate the FeB pairs [19,34,41]. The two lifetime measurements are then analysed to give the concentration of iron that exists in FeB pairs using an established method [30,40,42]. This concentration is henceforth referred to as the bulk iron concentration, and excludes iron present in other forms such as iron silicide precipitates, or iron bound to, or precipitated at, oxide precipitates and any surrounding defects. The specific analysis approach used is described in a previous publication [19]. The essential feature is that SRH statistics (Eq. (1)) are used with the recombination parameters of Rein and Glunz [30] to determine the bulk iron concentration required to account for a lifetime change at a given injection level. For the results presented in this paper, the injection level used was 0.2p0. The bulk iron concentrations in the \u201cuncontaminated\u201d samples were always \u22641.5\u00d71012 cm\u22123.", "measurement_extractions": [ { "quantity": "range 1013 cm\u22123 to 1016 cm\u22123", "unit": "cm\u22123", "measured_entity": "injection level range", "measured_property": null }, { "quantity": "10 min", "unit": "min", "measured_entity": "samples", "measured_property": "pre-anneal" }, { "quantity": "200 \u00b0C", "unit": "\u00b0C", "measured_entity": "samples", "measured_property": "pre-anneal" }, { "quantity": "\u223c50", "unit": null, "measured_entity": "close-up flashes", "measured_property": null }, { "quantity": "<20 ms", "unit": "ms", "measured_entity": "flashes of light", "measured_property": "aggregated illumination time" }, { "quantity": "more than 24 h", "unit": "h", "measured_entity": "second lifetime measurement", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "lifetime measurements", "measured_property": null }, { "quantity": "0.2p0", "unit": "p0", "measured_entity": "injection level", "measured_property": null }, { "quantity": "\u22641.5\u00d71012 cm\u22123", "unit": "cm\u22123", "measured_entity": "\u201cuncontaminated\u201d samples", "measured_property": "bulk iron concentrations" } ], "split": "train", "docId": "S0927024813003036-2011", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A method for preparing CdS nanoparticles within the porous confines of a mesoporous starch gel is described. This method utilises the combined colloidal and flexible chemical nature of a porous polysaccharide (i.e. starch) gel to limit CdS growth. The resulting hybrid gels can be dried to produce CdS/starch materials with high surface areas, predominantly mesoporous characteristics and scope for high CdS loading. The synthesis is conducted in aqueous alcoholic solutions without the need for expensive preparation techniques or additional protection/templating strategies. Materials were prepared at increasing CdS loadings on the starch gel, which confined nanoparticle growth and directed size/surface coverage, dispersion and UV\u2013vis absorption profile. The resulting powders presented large mesopore domains with high volumes (pore diameters > 10 nm; Vmeso > 0.5 cm3 g\u22121) and surface areas (SBET > 170 m2 g\u22121), interestingly effectively increasing with CdS loading. The synthesised CdS nanoparticles were characterised in the 5\u201340 nm range of a cubic crystalline structure, increasing in size with loading. A complete surface coverage of the starch gel structure occurs at a CdS/starch ratio = 1 (w/w), allowing the synthesis of a unique mesoporous CdS/polysaccharide hybrid. The presented route is simple, green and in principle extendable to a wide range of QDs and polysaccharide gels, whereby the porous polysaccharide gel acts as the deposition point of Cd2+, directing and stabilising both the growth of the inorganic CdS phase and the expanded high surface area polysaccharide form.", "measurement_extractions": [ { "quantity": "> 10 nm", "unit": "nm", "measured_entity": "mesopore domains", "measured_property": "pore diameters" }, { "quantity": "> 0.5 cm3 g\u22121", "unit": "cm3 g\u22121", "measured_entity": "mesopore domains", "measured_property": "Vmeso" }, { "quantity": "> 170 m2 g\u22121", "unit": "m2 g\u22121", "measured_entity": "mesopore domains", "measured_property": "SBET" }, { "quantity": "5\u201340 nm range", "unit": "nm", "measured_entity": "synthesised CdS nanoparticles", "measured_property": "characterised" }, { "quantity": "1 (w/w)", "unit": "w/w", "measured_entity": "CdS/starch", "measured_property": "ratio" } ], "split": "train", "docId": "S0927775713009606-1074", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Wide-angle diffraction patterns were acquired using a Bruker AXS D8 diffractometer with CuK\u03b1 (\u03bb = 1.5418 \u00c5), over a 2\u03b8 range from 5\u00b0 to 85\u00b0, using a step size of 0.1\u00b0 and a counting time per step of 4 s. CdS particle size was calculated using the Scherrer equation, assuming a spherical particle morphology and using a Gaussian peak fitting procedure to determine the FWHM (i.e. K = 0.9).", "measurement_extractions": [ { "quantity": "range from 5\u00b0 to 85\u00b0", "unit": "\u00b0", "measured_entity": "2\u03b8", "measured_property": null }, { "quantity": "0.1\u00b0", "unit": "\u00b0", "measured_entity": "diffractometer", "measured_property": "step size" }, { "quantity": "4 s", "unit": "s", "measured_entity": "step", "measured_property": "counting time" }, { "quantity": "0.9", "unit": null, "measured_entity": "Scherrer equation", "measured_property": "K" } ], "split": "train", "docId": "S0927775713009606-1216", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "It is important to note that the divalent cation Cd2+ has an ionic radius of 0.097 nm [22], which leads to a number of speculative points; (1) the diameter of the cation is smaller than the internal cavity of the amylose helix (double or single; ca. 0.4\u20131.0 nm), which itself is presumably flexible under the synthesis conditions to accommodate introduction of the Cd2+ into its hydrophobic cavity (perhaps occupying micropores (Table 1); (2) the diameter is also approximately the same length as the (OH) H-bonding distances (i.e. 0.19\u20130.21 nm) [23] between adjacent glucose residues that form the amylose helix (believed to be key to the formation of the porous polysaccharide phase); [15,24] consequently coordination of Cd2+ may stabilise an alternative metastable polysaccharide confirmation(s), gel state and in turn expanded surface properties. It is thought that at low loadings (e.g. CdS3), Cd2+ is adsorbed initially within the (smaller) pores of MS, leading to a nanoconfinement of the forming CdS phase. CdS will nucleate here presumably according to a pseudo-epitaxial growth, generating confined, amorphous nanoparticles or \u201cseeds\u201d (as indicated from XRD data). As CdS nanoparticles grow and fill the porous domains as loading increases, the polysaccharide structure remains flexible, particularly in the presence of water (or water/alcohol mixtures), and the hydrogen bond network may then rearrange accordingly to accommodate the inorganic phase. As the CdS clusters grow in size (and becoming increasingly crystalline) in the mesopores, the electrostatic repulsion increases the average pore size but with no reduction in surface area presumably as a consequence of an increased primary particle separation within the gel phase. This is an interesting observation and may lead to the synthesis of a variety of polysaccharide/inorganic hybrids and templated materials, as well as the potential to utilise ionic potential to direct textural properties of polysaccharide gels and associated porous xero- and aerogels.", "measurement_extractions": [ { "quantity": "0.097 nm", "unit": "nm", "measured_entity": "divalent cation Cd2+", "measured_property": "ionic radius" }, { "quantity": "0.19\u20130.21 nm", "unit": "nm", "measured_entity": "H-bonding", "measured_property": "distances" } ], "split": "train", "docId": "S0927775713009606-1361", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In each sub-figures (a)\u2013(f) of Fig. 4, the x-coordinate pertains to the ratio of the number of the inserted objects and original data, while the y-coordinate concerns the computational time. According to the experimental results as shown in Table 9 and Fig. 4, we find that the computational time of both static (Algorithm 1) and incremental (Algorithm 3) algorithms are increasing monotonically along with the increasing of insert ratios. It is easy to get the incremental algorithm is always faster than the static algorithm when the inserting ratio increases from 10% to 100% according to Fig. 4(a)\u2013(e). In Fig. 4(f), we find the incremental algorithm is mush faster than the static algorithm when the inserting ratio is less than 85%, but slower than the static algorithm when the inserting ratio is more than 85%.", "measurement_extractions": [ { "quantity": "from 10% to 100%", "unit": "%", "measured_entity": "inserting ratio", "measured_property": "increases" }, { "quantity": "less than 85%", "unit": "%", "measured_entity": "inserting ratio", "measured_property": null }, { "quantity": "more than 85%", "unit": "%", "measured_entity": "inserting ratio", "measured_property": null } ], "split": "train", "docId": "S0950705113001895-23699", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The following significance diagram displays the average rank of the classifiers at an 85% good, 15% bad class split:", "measurement_extractions": [ { "quantity": "85%", "unit": "%", "measured_entity": "classifiers", "measured_property": "good" }, { "quantity": "15%", "unit": "%", "measured_entity": "classifiers", "measured_property": "bad class" } ], "split": "train", "docId": "S095741741101342X-2624", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "AR comparison at an 85/15% split of good/bad observations.", "measurement_extractions": [ { "quantity": "85/15%", "unit": "%", "measured_entity": "AR comparison", "measured_property": "good/bad observations" } ], "split": "train", "docId": "S095741741101342X-726", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The generator efficiency, defined in Eq. (29), is presented in Fig. 13. Strategies A\u2013C lead to similar generator efficiencies, the two major trends being that increased average wind speed causes a higher generator efficiency while a higher frequency of the wind speed reduces the generator efficiency. In the high wind speed case at low frequencies, the generator efficiency of the reference strategy is similar to that of the other strategies. However, the generator efficiency with the reference strategy is lower at high wind frequencies. This reduced efficiency is due to high resistive loss caused by the high generator torque during the rapid oscillations. In the low wind speed case at wind frequencies of 0.01\u20130.03 Hz, the generator efficiency is higher with the reference strategy than with the other strategies, since strategies A\u2013C operate at too high rotational velocities and thereby increase the already dominant core loss. In the low wind speed case at wind frequencies above 0.003 Hz, the reference strategy causes a too low average tip speed ratio which reduces energy capture, see Figs. 10 and 11.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "major trends", "measured_property": null }, { "quantity": "0.01\u20130.03 Hz", "unit": "Hz", "measured_entity": "wind", "measured_property": "frequencies" }, { "quantity": "above 0.003 Hz", "unit": "Hz", "measured_entity": "wind", "measured_property": "frequencies" } ], "split": "train", "docId": "S0960148113002048-3775", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The boundary conditions were as follows. On the top, bottom and right boundaries a no-slip boundary condition was applied. On the left boundary a Dirichlet boundary condition enforced a sinusoidal in-/outflow velocity:u(x,y,t)=(\u22122sin(2\u03c0t/P)0),where P is the tidal period time. Due to the small basin size, a realistically long tidal period would lead to an excessively large tidal range. Therefore, the tidal period was defined to be P \u2261 10 min, which resulted in a tidal range of \u00b112 m. The simulation time was set to one full tidal period with a time step of \u0394t = 12 s. No spin up phase was applied, as its effect is assumed to be small due to the relatively short extent of the domain.", "measurement_extractions": [ { "quantity": "10 min", "unit": "min", "measured_entity": "tidal period", "measured_property": "P" }, { "quantity": "\u00b112 m.", "unit": "m", "measured_entity": "tidal range", "measured_property": null }, { "quantity": "12 s", "unit": "s", "measured_entity": "simulation time", "measured_property": "time step of \u0394t" } ], "split": "train", "docId": "S0960148113004989-3203", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The domain of the third scenario is shown in Fig. 3c. First, only the layout problem is solved. For this test, inequality constraints were applied to enforce a minimum distance of 30 m between each turbine. The optimisation algorithm terminated after 56 iterations (55 gradient evaluations, 73 functional evaluations). The optimised farm layout extracts 40.6 MW, which corresponds to an increase of 31% compared to the initial layout (30.9 MW) (Fig. 8c). The optimised layout features a distinct \u22c4-shaped alignment with an opening on the inflow facing side (Fig. 8b). Fig. 8d shows the velocity magnitude and suggests that this hole acts to trap and push the flow through the downstream turbines similar to the previous examples.", "measurement_extractions": [ { "quantity": "30 m", "unit": "m", "measured_entity": "third scenario", "measured_property": "minimum distance" }, { "quantity": "56", "unit": null, "measured_entity": "iterations", "measured_property": null }, { "quantity": "55", "unit": null, "measured_entity": "gradient evaluations", "measured_property": null }, { "quantity": "73", "unit": null, "measured_entity": "functional evaluations", "measured_property": null }, { "quantity": "40.6 MW", "unit": "MW", "measured_entity": "optimised farm layout", "measured_property": "extracts" }, { "quantity": "31%", "unit": "%", "measured_entity": "increase", "measured_property": null }, { "quantity": "30.9 MW", "unit": "MW", "measured_entity": "initial layout", "measured_property": "extracts" } ], "split": "train", "docId": "S0960148113004989-3258", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The final scenario (Fig. 3d) was solved with the non-stationary shallow water equations. The simulation time consisted of one P = 12 h sinusoidal period with a time step of \u0394t = 864 s. No spin up phase was applied, as its effect is assumed to be small due to the relatively short extent of the domain. Dirichlet boundary conditions on the left and right boundaries enforced the following sinusoidal in-/outflow velocity:u(x,y,t)=(\u22122sin(2\u03c0t/P)0).", "measurement_extractions": [ { "quantity": "12 h", "unit": "h", "measured_entity": "simulation time", "measured_property": "one P" }, { "quantity": "864 s", "unit": "s", "measured_entity": "one P = 12 h sinusoidal period", "measured_property": "time step of \u0394t" } ], "split": "train", "docId": "S0960148113004989-3277", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The averaged power extracted during one cycle increased by 22% from 48.4 MW to 59.0 MW (Fig. 10e). Since the computational domain is symmetric and the simulation time covered one full period, the optimised layout is expected to be symmetric in the x-direction. The numerical solution, shown in Fig. 10b, indeed shows an almost symmetric result. The turbine alignment consists of two distorted \u2228 shapes whose open ends face the in/-outflow boundaries. Similar to the previous example, an interpretation of this alignment is to divert the stream towards the corner of the \u2228 where turbines can extract large amounts of power. An additional row of turbines can be seen parallel to the bottom of the domain. These turbines are positioned to capture energy from the flow passing along the boundary.", "measurement_extractions": [ { "quantity": "one", "unit": null, "measured_entity": "cycle", "measured_property": null }, { "quantity": "22%", "unit": "%", "measured_entity": "one cycle", "measured_property": "averaged power extracted" }, { "quantity": "48.4 MW to 59.0 MW", "unit": "MW", "measured_entity": "one cycle", "measured_property": "averaged power extracted" }, { "quantity": "one", "unit": null, "measured_entity": "simulation time", "measured_property": "full period" }, { "quantity": "two", "unit": null, "measured_entity": "distorted \u2228 shapes", "measured_property": null } ], "split": "train", "docId": "S0960148113004989-3327", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Operating efficiency: sub-optimal control systems, misaligned components and electrical losses within the farm are found to reduce output by 2% in well-performing field installations relative to the turbine's supplied power curve [30];", "measurement_extractions": [ { "quantity": "2%", "unit": "%", "measured_entity": "farm", "measured_property": "output" } ], "split": "train", "docId": "S0960148113005727-1181", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The combined average of these measures is \u22120.43 \u00b1 0.05 percentage points per year, giving \u22121.6 \u00b1 0.2% annual degradation. The similarity of results from different methods gives us confidence that the underlying trend is robust: the decline in load factor with age is neither an artefact of systematic variation in wind speeds nor of the continual improvement in technology. Questions do however remain as to the exact form of this degradation, for example whether it is linear, quadratic or logarithmic with age; or how degradation rates are changing over time and whether they will be lower in the future. Access to data from more farms, and a more detailed wind resource assessment for each site will be fundamental to furthering our understanding of these issues.", "measurement_extractions": [ { "quantity": "\u22120.43 \u00b1 0.05 percentage points per year", "unit": "percentage points per year", "measured_entity": "measures", "measured_property": null }, { "quantity": "\u22121.6 \u00b1 0.2%", "unit": "%", "measured_entity": "annual degradation", "measured_property": null } ], "split": "train", "docId": "S0960148113005727-1494", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "This could have significant policy implications for the desirability of investing in wind power, as argued in a recent report by Hughes for the Renewable Energy Foundation (REF) [1]. That report suggested that the load factors of wind farms in the UK have declined by 5\u201313% per year, normalising for month-by-month variations in wind speeds. These findings could represent a significant hurdle for the wind industry, but they require replication.", "measurement_extractions": [ { "quantity": "5\u201313% per year", "unit": "% per year", "measured_entity": "wind farms in the UK", "measured_property": "load factors" } ], "split": "train", "docId": "S0960148113005727-904", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "KSS is a multisystem disorder defined clinically by the triad of PEO and pigmentary retinopathy with onset before the age of 20 years plus at least one of: cardiac conduction block; cerebrospinal fluid (CSF) protein concentration greater than 0.1 g/L; cerebellar ataxia [76\u201380]. Frequent additional signs include sensorineural hearing loss, renal tubular acidosis, dementia, seizures, short stature and endocrine disturbance (diabetes mellitus, hypoparathyroidism and growth hormone deficiency). A progressive skeletal myopathy is also frequently seen. The most common magnetic resonance imaging (MRI) findings are cerebral and cerebellar atrophy with bilateral, often symmetrical, hyperintense lesions in the subcortical white matter, thalamus, basal ganglia and brainstem [81\u201383]. Interestingly, there appears to be little correlation between neurological deficits and the severity of the MRI features [84]. KSS is most commonly associated with single large-scale deletions of mtDNA [56]. We recently identified a case of KSS caused by nuclear genomic dysfunction due to mutations in RRM2B in a patient with multiple deletions of the mtDNA [85]. It is, therefore, possible for KSS to follow a Mendelian pattern of inheritance, although the majority of cases are sporadic.", "measurement_extractions": [ { "quantity": "before the age of 20 years", "unit": "years", "measured_entity": "PEO and pigmentary retinopathy", "measured_property": "onset" }, { "quantity": "greater than 0.1 g/L", "unit": "g/L", "measured_entity": "cerebrospinal fluid (CSF)", "measured_property": "protein concentration" } ], "split": "train", "docId": "S0960896612001022-1223", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "To meet these challenges fertilizers recommendations to farmers become a common practice worldwide generally optimizing fertilizer doses to sustain a desired yield without a load to the environment. Consequently, soil P recommendation systems are widely used around the world to ensure good soil management and nutrient efficiency promoting agricultural sustainability. However recommendation systems differ considerably among countries. Not many systems can be found as peer reviewed literature; however a brief overview on those available is hereby given. Phosphorus recommendation systems are commonly used in Brazil, in a country where soils are generally nutrient poor. The Brazilian recommendation systems are based on quantitative analyses of soil input variables. The input variable consists of the following factors; cation exchange capacity (CEC), base saturation (BS), base sum, exchangeable aluminium (Al), calcium/magnesium (Ca/Mg), potassium (K) and P levels, sodium (Na) saturation and electrical conductivity. The output variable of the system is the amount of fertilizer to be applied. This is mainly based on 4 classes, low, medium, high to very high (Palhares et al., 2001). While Brazil follows a detailed set of variables when recommending P fertilizer levels, the agronomists at Kansas University \u2013 who, among other land grant Universities in the United States, provide single rate recommendation for nutrients such as P \u2013 are developing a fertilizer recommendation system that gives growers the flexibility to choose a soil management practice suitable for their needs. This flexibility included choosing from 2 systems, the \u201cnutrient sufficiency recommendation system\u201d which is developed to provide a 90\u201395% maximum yield for the year, and the \u201cbuild maintenance fertility program\u201d based soil test values over a planned period of time, usually 4\u20138 years, for both immediate crop needs and build up levels to a non-limiting value (Leikam et al., 2003). In West Africa, a framework to optimize soil fertility management in rice production is in use were the yield potential is estimated by an ecophysiological model based on weather conditions, cultivar species and sowing date. This yield potential is used as an input into a static model together with field specific data such as recovery efficiency of applied N, P and K, indigenous NPK supply and maximum NPK accumulation. Outputs of the framework include, required fertilizer doses to obtain different yield targets depending on yield potential and the soil nutrient supply (Haefele et al., 2003).", "measurement_extractions": [ { "quantity": "4", "unit": null, "measured_entity": "classes", "measured_property": null }, { "quantity": "90\u201395%", "unit": "%", "measured_entity": "nutrient sufficiency recommendation system", "measured_property": "maximum yield for the year" }, { "quantity": "4\u20138 years", "unit": "years", "measured_entity": "soil test values", "measured_property": "period of time" } ], "split": "train", "docId": "S1161030113001950-923", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Crystallite sizes were determined by a scanning electron microscope (LEO 1525 field emission scanning electron microscope, with a nominal resolution of 1.5 nm at 20 kV) equipped with a backscattering detector. For correlating the dilatometric length changes with modifications of microstructure, microscopy samples were prepared from the same part of the HPT disk as the dilatometric samples and subsequently annealed under identical conditions in the dilatometer up to predefined temperatures at a heating rate of 5 K/min, followed by rapid cooling to ambient temperature at a rate of about 30 K/min.", "measurement_extractions": [ { "quantity": "1.5 nm", "unit": "nm", "measured_entity": "LEO 1525 field emission scanning electron microscope", "measured_property": "nominal resolution" }, { "quantity": "20 kV", "unit": "kV", "measured_entity": "LEO 1525 field emission scanning electron microscope", "measured_property": null }, { "quantity": "5 K/min", "unit": "K/min", "measured_entity": "microscopy samples", "measured_property": "heating rate" }, { "quantity": "about 30 K/min", "unit": "K/min", "measured_entity": "microscopy samples", "measured_property": "rapid cooling to ambient temperature at a rate" } ], "split": "train", "docId": "S1359645413009816-2243", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Only a few experimental data are available in the literature for grain boundary expansion, primarily for isolated grain boundaries with a distinct orientation relation. From high-resolution transmission electron microscopy, values for Au of eGB=(0.04\u20130.10)\u00d710-10m (Ref. [22]) or eGB=0.12\u00d710-10m (Ref. [23]) are reported. From measurements of the grain boundary contact angle in an Al tricrystal, a value eGB=0.64\u00d710-10m is reported by Shvindlerman et al. [24] applying a thermodynamic model. For nanocrystalline Pd [25] and Fe [26], values of eGB=0.23\u00d710-10m and 0.19\u00d710-10m were determined from density measurements and modeling of grain growth kinetics, respectively. A number of computer simulations of grain boundaries [27\u201330] deal with the issue of grain boundary expansion. Here, however, the choice of the interatomic potentials was found to have a substantial influence on the numerical results [27]. Most recently, grain boundary expansion data have been reported from molecular dynamics simulations on Ni, eGB=(0.28\u20130.42)\u00d710-10m for random high-angle grain boundaries [30] and eGB=(0.39\u20130.41)\u00d710-10m (at T = 1200 K) for \u03a35 grain boundaries [29]. Here, the matching of the data values with the data of the dilatometric studies of Cu and Ni is remarkable.", "measurement_extractions": [ { "quantity": "(0.28\u20130.42)\u00d710-10m", "unit": "m", "measured_entity": "molecular dynamics simulations on Ni", "measured_property": "eGB" }, { "quantity": "(0.39\u20130.41)\u00d710-10m", "unit": "m", "measured_entity": "molecular dynamics simulations on Ni", "measured_property": "eGB" }, { "quantity": "1200 K", "unit": "K", "measured_entity": "molecular dynamics simulations on Ni", "measured_property": "T" } ], "split": "train", "docId": "S1359645413009816-2973", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Our model consists of a two-dimensional square system, with pre-set side length of L = 1 m, containing N = 1000 discrete particles. The overall coverage of particles in the material is given by the number density, \u03bb = N/L2 (fixed at 1000 m\u22122) and the area fraction, Af=\u03bba\u00af, where a\u00af is the mean cross-sectional area of a particle. Periodic boundary conditions are applied to the edges of the system to reduce finite size effects [22]. Our choice of units is irrelevant to the dispersion quality.", "measurement_extractions": [ { "quantity": "1 m", "unit": "m", "measured_entity": "two-dimensional square system", "measured_property": "side length" }, { "quantity": "1000 discrete particles", "unit": "discrete particles", "measured_entity": "two-dimensional square system", "measured_property": null }, { "quantity": "1000 m\u22122", "unit": "m\u22122", "measured_entity": "number", "measured_property": "density" } ], "split": "train", "docId": "S1359835X13001875-1359", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Molar ratios of 100Ti/Al obtained from the suspended particles in the air. The data were collected during Asian Dust Storm (ADS) episodes and non-ADS periods in East China Sea and in Taipei. The collected particles include 2.5\u201310 \u03bcm (PM10) and less than 2.5 \u03bcm (PM2.5).", "measurement_extractions": [ { "quantity": "2.5\u201310 \u03bcm", "unit": "\u03bcm", "measured_entity": "collected particles", "measured_property": null }, { "quantity": "less than 2.5 \u03bcm", "unit": "\u03bcm", "measured_entity": "collected particles", "measured_property": null } ], "split": "train", "docId": "S1367912013002277-1213", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "To deliberate the chirality of the supramolecular architecture, solid state circular dichroism spectra were measured in a KCl matrix for GMP ligand and the single-crystals of complex 1 (Fig. 3(b)). There is a red-shift in the solid state CD spectrum of GMP compared with its liquid CD spectrum. The negative band about the conformation of the sugar moiety has nearly disappeared. In solid-state, mutarotation is more difficult and the negative band at the 260 nm nearby can be detected which indicates that the GMP ligand is mainly \u03b2-anomers. In the spectrum of complex 1, the weak negative band at 215\u2013225 nm indicates that the GMP exists as l-ribo [5], which is induced by the strong \u03c0\u2013\u03c0 and hydrogen bonding interaction. For solid samples, CD spectroscopy is highly sensitive to even a very small distortion from planarity of the aromatic chromophore [12]. The strong negative CE centered at 297 nm (\u03b8 \u2248 \u2212 10 mdeg) is due to the excitation coupling of \u03c0 \u2192 \u03c0* transitions of the aromatic chromophores, including the intra- and intermolecular coupling of the guanine chromophores [13], which is consistent with the single crystal structure.", "measurement_extractions": [ { "quantity": "260 nm", "unit": "nm", "measured_entity": "solid state CD spectrum of GMP", "measured_property": "negative band" }, { "quantity": "215\u2013225 nm", "unit": "nm", "measured_entity": "spectrum of complex 1", "measured_property": "negative band" }, { "quantity": "297 nm", "unit": "nm", "measured_entity": "strong negative CE", "measured_property": null }, { "quantity": "\u2248 \u2212 10 mdeg", "unit": "mdeg", "measured_entity": "\u03b8", "measured_property": null } ], "split": "train", "docId": "S1387700313001822-661", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Rate-capability of (a) NaFeO2 [12], (b) NaFe0.5Co0.5O2, and (c) NaCoO2 in Na cells. The cells were charged to 4.0 V at a rate of 12 mA g\u2212 1 and then discharged at different rates; 1/20 (12 mA g\u2212 1)\u201330C (7260 mA g\u2212 1). The sample loading on Al foil was (a) 1.3 (b) 2.4, and (c) 2.2 mg cm\u2212 2 as the active material.", "measurement_extractions": [ { "quantity": "4.0 V", "unit": "V", "measured_entity": "cells", "measured_property": "charged" }, { "quantity": "12 mA g\u2212 1", "unit": "mA g\u2212 1", "measured_entity": "cells", "measured_property": "rate" }, { "quantity": "1/20 (12 mA g\u2212 1)\u201330C (7260 mA g\u2212 1)", "unit": "mA g\u2212 1", "measured_entity": "cells", "measured_property": "rates" }, { "quantity": "(a) 1.3 (b) 2.4, and (c) 2.2 mg cm\u2212 2", "unit": "mg cm\u2212 2", "measured_entity": "sample", "measured_property": null } ], "split": "train", "docId": "S1388248113001951-339", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The results in Table 3 indicate that when there is no attack RED has no obvious influence on the normal TCP flows (\u03c1na and rna are almost the same for DropTail and RED). However, when LDDoS attacks are present, RED significantly improves the throughput of normal TCP. Note that in this case, RED drops 25% packets, much more than the 7.6% from DropTail. In other words, when the aggregate rate of the LDDoS flows is very close to the bottleneck bandwidth of network, deploying RED is able to drop more attack packets while increasing the throughput of the normal TCP flows. Admittedly RED might also drop more legitimate packets than DropTail here, however it achieves a higher normal-TCP-flow throughput \u03c1a than DropTail. The throughput \u03c1a of normal TCP flows is considered to be more important than its drop ratio ra when a network is under an LDDoS attack.", "measurement_extractions": [ { "quantity": "25%", "unit": "%", "measured_entity": "packets", "measured_property": "RED drops" }, { "quantity": "7.6%", "unit": "%", "measured_entity": "packets", "measured_property": "DropTail" } ], "split": "train", "docId": "S1389128612002496-5994", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Fig. 12 shows the results for the AWI LDDoS attack. Our CPR-based approach is still effective \u2013 the difference in the average CPR for normal TCP flows and LDDoS flows is evident. However, as Tb decreases (from 10 ms to 0.1 ms), the average CAS of the attack flows under the CAS-based approach also decreases. When Tb is around 0.5 ms, the average CAS of the normal TCP flows and the LDDoS flows is about the same.", "measurement_extractions": [ { "quantity": "from 10 ms to 0.1 ms", "unit": "ms", "measured_entity": "Tb", "measured_property": null }, { "quantity": "around 0.5 ms", "unit": "ms", "measured_entity": "Tb", "measured_property": null } ], "split": "train", "docId": "S1389128612002496-6119", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Fig. 13 shows the results for the ARI LDDoS attack. Similar to previous results, our CPR-based approach is still effective whereas the effectiveness of the CAS-based approach decreases as Rb decreases (from 0.25 Mbps to 0.01 Mbps).", "measurement_extractions": [ { "quantity": "from 0.25 Mbps to 0.01 Mbps", "unit": "Mbps", "measured_entity": "Rb", "measured_property": "decreases" } ], "split": "train", "docId": "S1389128612002496-6138", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We recruited 147 patients from consanguineous pedigrees (parents of the proband were second cousins or more closely related) who were diagnosed with permanent diabetes before 6 months of age or were diagnosed with permanent diabetes before 9 months and had any additional clinical features that made a diagnosis of type 1 diabetes less likely. Informed consent was obtained from all participants or their parents, and institutional review board approval was received for this study.", "measurement_extractions": [ { "quantity": "147", "unit": null, "measured_entity": "patients", "measured_property": null }, { "quantity": "before 6 months", "unit": "months", "measured_entity": "147 patients from consanguineous pedigrees", "measured_property": "diagnosed with permanent diabetes" }, { "quantity": "before 9 months", "unit": "months", "measured_entity": "147 patients from consanguineous pedigrees", "measured_property": "diagnosed with permanent diabetes" } ], "split": "train", "docId": "S1550413113004920-1509", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Input to the modeling consisted of the compressional wave velocity (Vp), shear wave velocity (Vs), and density (\u03c1) near the injection well obtained from borehole logging data (Fig. 4). Vp and \u03c1 were vertically averaged from the logs to remove high frequency fluctuations. Vs values from the well Ktzi202/2007 were vertically averaged over the main lithological units (F\u00f6rster et al., 2010). The resulting Vs model was linearly interpolated to the injection well using the interpreted lithological horizons after Kling (2011). The input wavelet was extracted from the 3D seismic baseline data (Juhlin et al., 2007) (Fig. 4), yielding a dominant frequency of 40 Hz. Seismic modeling with the reflectivity method using the previously described Vp, Vs and density models as input parameters resulted in a synthetic trace corresponding to a 3D surface seismic baseline trace near the injection well (Fig. 4).", "measurement_extractions": [ { "quantity": "40 Hz", "unit": "Hz", "measured_entity": "dominant frequency", "measured_property": null } ], "split": "train", "docId": "S175058361300203X-1280", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Next we apply the multi-phase fluid flow simulations of Lengler et al. (2010) to account for the lateral variability in the petrophysical properties of the storage formation at Ketzin. Lengler et al. (2010) performed simulations on multiple realizations of the Ketzin reservoir using a stochastic Monte Carlo approach in order to take into account the high degree of uncertainty in the reservoir characterization at the scale required for fluid flow simulations. In this paper, we use a similar approach, but this time to investigate the impact of the reservoir temperature on the fluid migration and, in turn, on the 4D seismic data. Hydrogeological studies at the Ketzin site (Norden et al., 2010) have shown that a 2D radially symmetric model of the upper part (33 m) of the Stuttgart Formation can be used to interpret the 3D data acquired near the injection well (Fig. 7). This model accounts for the presence of channel sandstones in the reservoir that are the most favorable for CO2 migration and contains effective porosities in the range of 20\u201325% (F\u00f6rster et al., 2010). As known from core and log analysis of the injection well Ktzi201/2007 and the first observation well Ktzi200/2007, the reservoir is composed of two high porosity sandstone layers. These layers are separated by a thin strongly cemented sandstone layer (Norden et al., 2010). Since the thickness of this layer is in the decimeter range, it cannot be detected with the seismic wavelengths typically available from surface-seismic measurements. However, this layer is known to be a significant constraint to fluid migration due to its low permeability (Wiese et al., 2010).", "measurement_extractions": [ { "quantity": "33 m", "unit": "m", "measured_entity": "upper part", "measured_property": null }, { "quantity": "20\u201325%", "unit": "%", "measured_entity": "channel sandstones", "measured_property": "effective porosities" }, { "quantity": "two", "unit": null, "measured_entity": "high porosity sandstone layers", "measured_property": null } ], "split": "train", "docId": "S175058361300203X-1483", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "It is well known that the seismic velocity in sandstones saturated with brine does not depend on temperatures in the range of 34\u201338 \u00b0C (e.g. Mavko, 2005). To demonstrate this we calculated the difference in Vp between both temperature scenarios present at the Ketzin site at the time of the 1st 3D seismic repeat campaign (Ivanova et al., 2012) using Gassmann's equations (1951) for 50% CO2 saturation. The resulting difference between the scenarios in Vp is less than 5 m/s. Considering the uncertainties (e.g. \u00b15% error in CO2 saturation) in the petrophysical experiments (Kummerow and Spangenberg, 2011; Ivanova et al., 2012) corresponding to \u00b170 m/s in Vp (Fig. 5), we did not take into account the Vp changes (Fig. 5, Eq. (1)) due to the different temperature scenarios (34 and 38 \u00b0C).", "measurement_extractions": [ { "quantity": "range of 34\u201338 \u00b0C", "unit": "\u00b0C", "measured_entity": "seismic velocity in sandstones saturated with brine does not depend on", "measured_property": "temperatures" }, { "quantity": "50%", "unit": "%", "measured_entity": "CO2 saturation", "measured_property": null }, { "quantity": "less than 5 m/s", "unit": "m/s", "measured_entity": "scenarios", "measured_property": "Vp" }, { "quantity": "\u00b15%", "unit": "%", "measured_entity": "CO2 saturation", "measured_property": "error" }, { "quantity": "\u00b170 m/s", "unit": "m/s", "measured_entity": "petrophysical experiments", "measured_property": "Vp" }, { "quantity": "34 and 38 \u00b0C", "unit": "\u00b0C", "measured_entity": "scenarios", "measured_property": "temperature" } ], "split": "train", "docId": "S175058361300203X-1542", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The resulting synthetic seismic differences (Fig. 8) of both the 34 \u00b0C and 38 \u00b0C options look very similar and also show some similarity to the real data, also shown in Fig. 8. The synthetic difference (repeat-base) seismograms from near the top of the reservoir agree reasonably well with the real difference seismograms (repeat-base) for the Ktzi201/2007 and Ktzi200/2007 wells reported by Ivanova et al. (2012). However, obvious disagreements are found at the Ktzi202/2007 well, which may be due to the fact that the velocity model used at this location is not sufficiently correct. Seismic amplitude differences between the 38 \u00b0C and 34 \u00b0C scenarios correspond to less than 1% of the amplitude values of the baseline (Fig. 8). Since the normalized root mean square (NRMS) differences in the 3D time-lapse data are greater than 10% (Kashubin et al., 2011) these temperature effects in the reservoir will not be resolvable with surface seismic methods at the Ketzin site.", "measurement_extractions": [ { "quantity": "34 \u00b0C", "unit": "\u00b0C", "measured_entity": "option", "measured_property": null }, { "quantity": "38 \u00b0C", "unit": "\u00b0C", "measured_entity": "option", "measured_property": null }, { "quantity": "38 \u00b0C", "unit": "\u00b0C", "measured_entity": "scenario", "measured_property": null }, { "quantity": "34 \u00b0C", "unit": "\u00b0C", "measured_entity": "scenario", "measured_property": null }, { "quantity": "less than 1%", "unit": "%", "measured_entity": "amplitude values of the baseline", "measured_property": "Seismic amplitude differences between the 38 \u00b0C and 34 \u00b0C scenarios" }, { "quantity": "greater than 10%", "unit": "%", "measured_entity": "normalized root mean square (NRMS) differences", "measured_property": null } ], "split": "train", "docId": "S175058361300203X-1556", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "By integrating seismic modeling and multiphase fluid flow simulations, we have estimated the impact of the reservoir temperature on the 4D seismic data from Ketzin. We studied two cases, one where the injection was performed at 34 \u00b0C and the other at 38 \u00b0C. Results from the multiphase fluid flow simulations show that the difference between the two cases is small for the CO2 migration. Likewise, the temperature does not affect significantly the seismic amplitude response, although the CO2 density is considerably lower for the higher temperature case. The difference in CO2 density between 34 \u00b0C and 38 \u00b0C decreases with decreasing pressure and, therefore, with increasing distance from the injection well. Therefore, the modeled time-lapse seismic differences for the two temperature scenarios is minor regarding the qualitative analysis of the 4D seismic data from the Ketzin CO2 storage site (Fig. 8).", "measurement_extractions": [ { "quantity": "34 \u00b0C and 38 \u00b0C", "unit": "\u00b0C", "measured_entity": "reservoir temperature", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "cases", "measured_property": null }, { "quantity": "34 \u00b0C", "unit": "\u00b0C", "measured_entity": "injection", "measured_property": null }, { "quantity": "38 \u00b0C", "unit": "\u00b0C", "measured_entity": "injection", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "cases", "measured_property": null } ], "split": "train", "docId": "S175058361300203X-1638", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "An additional uncertainty arises from saturation profiling, which discerns between pores filled with brine and pores filled with CO2 above a poorly constrained level of saturation (Arts et al., 2004). It follows from the commonly used Gassmann equation (Gassmann, 1951) that: (a) very low saturations of less than a few percent are undetectable; (b) a strong correlation emerges as the gas saturation increases from a few percent to around thirty percent; and (c) for saturations much above thirty percent, it is difficult to distinguish between moderate and high saturations. It follows that the 80% gas saturation that is commonly assumed for the Sleipner plume, while reasonable (Chadwick et al., 2005; Bickle et al., 2007), remains uncertain (Lumley, 2008). If the 80% assumption represents a reasonable upper limit to the mean saturation for the plume, the lower limit could be as low as 40%, halving mass balance estimates premised on the widely assumed high-saturation value.", "measurement_extractions": [ { "quantity": "a few percent to around thirty percent", "unit": "percent", "measured_entity": "gas saturation", "measured_property": null }, { "quantity": "above thirty percent", "unit": "percent", "measured_entity": "saturations", "measured_property": null }, { "quantity": "80%", "unit": "%", "measured_entity": "Sleipner plume", "measured_property": "gas saturation" }, { "quantity": "80%", "unit": "%", "measured_entity": "mean saturation for the plume", "measured_property": "upper limit" }, { "quantity": "40%", "unit": null, "measured_entity": "mean saturation for the plume", "measured_property": "lower limit" } ], "split": "train", "docId": "S1750583613004192-1267", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The Sleipner plume profile, viewed from the southeast, circa July 2002. The figure shows the distribution of injected CO2 after 5 Mt of injected CO2 (Bickle et al., 2007). The nine plume layers are well defined aerially, having ponded beneath intra-formational shales within the Utsira Sand. The uncertainty relating to layer thickness is high. The ninth layer first appeared in 1999, indicating that the CO2 has migrated approximately 220 m vertically from the injection point (IP) at 1012 mbsl in 3 years. The left panel is a regional stratigraphic column: MSL, Mean Sea Level; SWI, Sediment Water Interface; The Nordland Group extends from the caprock to seafloor, and is subdivided into three seals: US, Upper Seal (Pleistocene); MS, Middle Seal (Upper Pliocene); LS, Lower Seal (Upper Pliocene); Utsira, storage site (Middle Miocene\u2013Early Pliocene); HG, Hordland Group (Early Miocene).", "measurement_extractions": [ { "quantity": "circa July 2002", "unit": null, "measured_entity": "Sleipner plume profile", "measured_property": null }, { "quantity": "5 Mt", "unit": "Mt", "measured_entity": "CO2", "measured_property": "injected" }, { "quantity": "nine", "unit": null, "measured_entity": "plume layers", "measured_property": null }, { "quantity": "1999", "unit": null, "measured_entity": "ninth layer", "measured_property": "first appeared" }, { "quantity": "approximately 220 m", "unit": "m", "measured_entity": "CO2", "measured_property": "migrated" }, { "quantity": "1012 mbsl", "unit": "mbsl", "measured_entity": "injection point (IP)", "measured_property": null }, { "quantity": "3 years", "unit": "years", "measured_entity": "CO2", "measured_property": "migrated" } ], "split": "train", "docId": "S1750583613004192-714", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "It has not been reported whether the significantly simplified E8 medium is sufficient to support the survival and proliferation of undifferentiated hPSCs in suspension culture. Thus our studies began by establishing a static suspension culture condition in E8 medium for serial passaging and expansion of hiPSCs. BC1 cells cultured on E8 feeder-free conditions (E8-Matrigel or E8-VNT-N) for at least 3 passages were seeded as a single cell suspension in E8 medium supplemented with Y27632 on the first day (Watanabe et al., 2007). The cells survived and formed convex dish-shaped aggregates within 24 h due to the gravity force (Fig. 1a). The size of the cell aggregates gradually increased corresponding to the cell number along the culture period. BC1 cells expanded in static suspension in E8 medium with an average rate of 3.7 \u00b1 0.9 fold per passage, 3.7 \u00d7 106 fold increase in total with > 99% of the cells being TRA-1\u201360+ after 13 passages (Fig. 1b,c). We also examined the differentiation potential of expanded cells, using the spin-EB method (Ng et al., 2008, 2005; Yu et al., 2008). We found that under the hematopoiesis-inducing condition, leukocyte-like cells emerged around day 10 (Fig. 1d) with 46.8% \u00b1 1.6% of the cells becoming CD34+CD45+ HPCs (Fig. 1e). These results indicate that undifferentiated hiPSCs can be expanded in suspension in E8 medium supplemented with one-day treatment of Y27632, and retain their differentiation potential.", "measurement_extractions": [ { "quantity": "at least 3 passages", "unit": "passages", "measured_entity": "BC1 cells", "measured_property": "cultured on E8 feeder-free conditions" }, { "quantity": "within 24 h", "unit": "h", "measured_entity": "cells", "measured_property": "survived and formed convex dish-shaped aggregates" }, { "quantity": "3.7 \u00b1 0.9 fold per passage", "unit": "fold per passage", "measured_entity": "BC1 cells", "measured_property": "rate" }, { "quantity": "3.7 \u00d7 106 fold", "unit": "fold", "measured_entity": "BC1 cells", "measured_property": null }, { "quantity": "> 99%", "unit": "%", "measured_entity": "cells", "measured_property": "TRA-1\u201360+" }, { "quantity": "13", "unit": null, "measured_entity": "passages", "measured_property": null }, { "quantity": "46.8% \u00b1 1.6%", "unit": "%", "measured_entity": "cells", "measured_property": "CD34+CD45+ HPCs" }, { "quantity": "one-day", "unit": "day", "measured_entity": "undifferentiated hiPSCs", "measured_property": "treatment" } ], "split": "train", "docId": "S1873506113001116-1204", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "BC1 and TNC1 hiPSCs cultured in spinner flasks for more than 10 passages were tested for their pluripotency by in vitro and in vivo differentiation methods. For in vitro assay, by simply replacing the entire E8 medium with differentiation medium of day-2 spinner flask suspension culture, BC1 and TNC1 aggregates transformed into EBs and started spontaneous differentiation under the stimulation of 10% serum (Supplementary Fig. S4). After 8 days in spinner flask followed by 4 days on gelatin-coated tissue culture plates in differentiation medium, cells of all three germ layers could be detected by immunofluorescent staining of specific markers (Fig. 5a). In contrast, undifferentiated hiPSCs on day 0 showed negative staining of all germ markers (data not shown). For in vivo assay, BC1 and TNC1 harvested from suspension culture in spinner flasks were injected into immune-deficient mice and were able to form teratomas containing cells of all germ layers, including glandular epithelium, chondrocytes, and neural rosettes (Fig. 5b). Directed hematopoietic differentiation potential was also tested as previously described (Fig. 5c). Differentiating BC1 and TNC1 cells contained 43.57% \u00b1 4.35% (n = 3) and 43.22% \u00b1 7.13% (n = 3) CD34+CD45+ HPCs on day 14 of differentiation, respectively. The CFU assay measuring hematopoietic progenitors showed that hiPSCs cultured in spinner flasks were able to generate colonies of different hematopoietic cell lineages. The total number of CFUs was comparable or significantly larger (P = 0.0307, n = 3) than cells cultured in parallel in adhesion cultures (Fig. 5d).", "measurement_extractions": [ { "quantity": "more than 10 passages", "unit": "passages", "measured_entity": "BC1 and TNC1 hiPSCs", "measured_property": "cultured in spinner flasks" }, { "quantity": "10%", "unit": "%", "measured_entity": "serum", "measured_property": null }, { "quantity": "After 8 days", "unit": "days", "measured_entity": "cells of all three germ layers", "measured_property": "spinner flask" }, { "quantity": "4 days", "unit": "days", "measured_entity": "cells of all three germ layers", "measured_property": "gelatin-coated tissue culture plates in differentiation medium" }, { "quantity": "43.57% \u00b1 4.35%", "unit": "%", "measured_entity": "BC1", "measured_property": "CD34+CD45+ HPCs" }, { "quantity": "3", "unit": null, "measured_entity": "BC1", "measured_property": "n" }, { "quantity": "43.22% \u00b1 7.13%", "unit": "%", "measured_entity": "TNC1", "measured_property": "CD34+CD45+ HPCs" }, { "quantity": "3", "unit": null, "measured_entity": "TNC1", "measured_property": "n" }, { "quantity": "0.0307", "unit": null, "measured_entity": "total number of CFUs", "measured_property": "P" }, { "quantity": "3", "unit": null, "measured_entity": "total number of CFUs", "measured_property": "n" } ], "split": "train", "docId": "S1873506113001116-1369", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Serial passaging of hiPSCs in static suspension in E8 medium. (a) Light microscope image of aggregates after 48 h post-inoculation of single suspension BC1 cells in ultra-low attachment plates. (b) BC1 cells cultured in E8 medium, expanded in static suspension culture for 13 passages (45 days, n = 3). (c) Flow cytometry plots (n = 3) of TRA-1\u201360 expression in hiPSCs after 13 passages in static suspension. (d) Light microscope image of HPC-like single suspending cells emerging around a spin-EB on day 14. (e) Flow cytometry plots (n = 2) of CD34 and CD45 expression in day-14 spin EBs. NC = negative control. Scale bars = 200 \u03bcm.", "measurement_extractions": [ { "quantity": "after 48 h", "unit": "h", "measured_entity": "aggregates", "measured_property": "Light microscope image" }, { "quantity": "13 passages", "unit": "passages", "measured_entity": "BC1 cells cultured in E8 medium", "measured_property": "expanded in static suspension culture" }, { "quantity": "45 days", "unit": null, "measured_entity": "13 passages", "measured_property": null }, { "quantity": "3", "unit": null, "measured_entity": "13 passages", "measured_property": "n" }, { "quantity": "3", "unit": null, "measured_entity": "Flow cytometry plots", "measured_property": "n" }, { "quantity": "after 13 passages", "unit": "passages", "measured_entity": "static suspension", "measured_property": null }, { "quantity": "200 \u03bcm", "unit": "\u03bcm", "measured_entity": "Scale bars", "measured_property": null } ], "split": "train", "docId": "S1873506113001116-710", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Large-scale production of human induced pluripotent stem cells (hiPSCs) by robust and economic methods has been one of the major challenges for translational realization of hiPSC technology. Here we demonstrate a scalable culture system for hiPSC expansion using the E8 chemically defined and xeno-free medium under either adherent or suspension conditions. To optimize suspension conditions guided by a computational simulation, we developed a method to efficiently expand hiPSCs as undifferentiated aggregates in spinner flasks. Serial passaging of two different hiPSC lines in the spinner flasks using the E8 medium preserved their normal karyotype and expression of undifferentiated state markers of TRA-1\u201360, SSEA4, OCT4, and NANOG. The hiPSCs cultured in spinner flasks for more than 10 passages not only could be remained pluripotent as indicated by in vitro and in vivo assays, but also could be efficiently induced toward mesodermal and hematopoietic differentiation. Furthermore, we established a xeno-free protocol of single-cell cryopreservation and recovery for the scalable production of hiPSCs in spinner flasks. This system is the first to enable an efficient scale-up bioprocess in completely xeno-free condition for the expansion and cryopreservation of hiPSCs with the quantity and quality compliant for clinical applications.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "hiPSC lines", "measured_property": null }, { "quantity": "10 passages", "unit": "passages", "measured_entity": "hiPSCs", "measured_property": "cultured" } ], "split": "train", "docId": "S1873506113001116-978", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The differentiation medium was changed to fresh medium 48 h before the assay. Albumin secretion was measured by the central clinical laboratory at Kumamoto University, Kumamoto, Japan.", "measurement_extractions": [ { "quantity": "48 h", "unit": "h", "measured_entity": "differentiation medium", "measured_property": "changed" } ], "split": "train", "docId": "S1873506114000075-1132", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For cell differentiation, FAP-specific iPS cells were cultured with serial changes of media as shown in Fig. 3A. To test whether FAP-specific iPS cells can differentiate into hepatocyte-like cells, we analyzed several markers via real-time PCR analysis on Day 5 (D5), D13 and D20 differentiated FAP-specific iPS cells (Fig. 3B). A decrease in expression of the pluripotency marker Oct3/4 was accompanied by differentiation of FAP-specific iPS cells. Expression of the endoderm marker Sox17 was observed on D5 differentiation and decreased gradually after the medium was changed to hepatic differentiation medium on D7 (Fig. 3B). The hepatic progenitor marker AFP and the mature hepatocyte marker ALB were obviously expressed on D13 and D20. In addition, immunocytochemical analyses showed Sox17 expression on D5, both HNF-4\u03b1 and AFP expression on D13, and ALB cytoplasmic staining on D20 (Fig. 3C). Quantitative imaging analysis revealed that approximately 78 \u00b1 0.6% of cells were Sox17-positive on D5 and approximately 88 \u00b1 1.1% of cells were AFP-positive on D13 and approximately 29 \u00b1 0.9% of cells were ALB-positive on D20 (Fig. 3D). The ALB secretion in the media of differentiated FAP-specific iPS cells on D20 was approximately 20 \u03bcg/ml (Fig. 3E). Moreover, these D20 differentiated FAP-specific iPS cells were also periodic acid-Schiff (PAS)-positive, indicating cytoplasmic glycogen storage (Fig. 3F). These results clearly indicated that FAP-specific iPS cells had the potential to differentiate into hepatocyte-like cells.", "measurement_extractions": [ { "quantity": "approximately 78 \u00b1 0.6%", "unit": "%", "measured_entity": "cells", "measured_property": "Sox17-positive on D5" }, { "quantity": "approximately 88 \u00b1 1.1%", "unit": "%", "measured_entity": "cells", "measured_property": "AFP-positive on D13" }, { "quantity": "approximately 29 \u00b1 0.9%", "unit": "%", "measured_entity": "cells", "measured_property": "ALB-positive on D20" }, { "quantity": "approximately 20 \u03bcg/ml", "unit": "\u03bcg/ml", "measured_entity": "ALB secretion", "measured_property": null } ], "split": "train", "docId": "S1873506114000075-1242", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "This study was approved by the Institutional Review Board and Ethics Committees of the University of California, San Francisco, and written informed consent was obtained in all cases. The patient with the PGRN S116X mutation followed the classic clinical progression for FTD and developed parkinsonism, as do all FTD patients with PGRN mutations, but he did not show typical features of PD dementia. The patient with sporadic FTD also showed parkinsonism. Skin biopsies were collected, cut into small pieces, and placed on culture dishes to allow the fibroblasts to expand. The cells were maintained in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum, 1X nonessential amino acids, and penicillin/streptomycin (100 U/ml). iPSCs were generated as described previously (Takahashi et al., 2007). Please see Supplemental Information for more details.", "measurement_extractions": [ { "quantity": "10%", "unit": "%", "measured_entity": "Dulbecco's modified Eagle's medium", "measured_property": "fetal bovine serum" }, { "quantity": "100 U/ml", "unit": "U/ml", "measured_entity": "Dulbecco's modified Eagle's medium", "measured_property": "penicillin/streptomycin" } ], "split": "train", "docId": "S2211124712002884-1060", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "iPSC colonies were detached with accutase (Millipore) and grown as embryoid bodies (EBs) in suspension for 5\u20136 days in iPSC medium without basic fibroblast growth factor. EBs were allowed to attach and form rosettes. Ten-day-old rosettes were collected and grown in suspension as neurospheres. Neurospheres were dissociated after 3\u20134 weeks, and the cells were plated on glass coverslips (BD Biosciences) or plates coated with poly-D-lysine (0.1 mg/ml) and laminin (10 \u03bcg/m). Neurons were used after 2-4 weeks in culture.", "measurement_extractions": [ { "quantity": "5\u20136 days", "unit": "days", "measured_entity": "iPSC colonies", "measured_property": "grown as embryoid bodies (EBs) in suspension" }, { "quantity": "Ten-day", "unit": "day", "measured_entity": "rosettes", "measured_property": "old" }, { "quantity": "after 3\u20134 weeks", "unit": "weeks", "measured_entity": "Neurospheres", "measured_property": "dissociated" }, { "quantity": "0.1 mg/ml", "unit": "mg/ml", "measured_entity": "poly-D-lysine", "measured_property": null }, { "quantity": "10 \u03bcg/m", "unit": "\u03bcg/m", "measured_entity": "laminin", "measured_property": null }, { "quantity": "after 2-4 weeks", "unit": "weeks", "measured_entity": "Neurons", "measured_property": "used" } ], "split": "train", "docId": "S2211124712002884-1110", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(F) Intracellular PGRN levels in iPSC-derived neurons after medium collection. Values of control line 20 were set to 100% (n = 3\u20134 independent cultures).", "measurement_extractions": [ { "quantity": "100%", "unit": "%", "measured_entity": "Values of control line 20", "measured_property": "Intracellular PGRN levels" }, { "quantity": "3\u20134", "unit": null, "measured_entity": "independent cultures", "measured_property": "n" } ], "split": "train", "docId": "S2211124712002884-649", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(C) Control lines 16 and 17, sporadic lines 12 and 23, and PGRN S116X lines 1 and 14 were immunostained for AFP (endoderm), desmin (mesoderm), and \u03b2III-tubulin (ectoderm), and counterstained with DAPI (nuclei). All lines showed a normal karyotype. Scale bar: 50 \u03bcm.", "measurement_extractions": [ { "quantity": "50 \u03bcm", "unit": "\u03bcm", "measured_entity": "Scale bar", "measured_property": null } ], "split": "train", "docId": "S2211124712002884-682", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(D) PGRN secreted into the medium over 24 hr of microglia derived from control, sporadic, and PGRN S116X iPSCs. The average of values for control lines was set to 100%. Values are mean \u00b1 SEM.", "measurement_extractions": [ { "quantity": "24 hr", "unit": "hr", "measured_entity": "microglia derived from control, sporadic, and PGRN S116X iPSCs", "measured_property": null }, { "quantity": "100%", "unit": "%", "measured_entity": "control lines", "measured_property": "average of value" } ], "split": "train", "docId": "S2211124712002884-705", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The two FTD patients under investigation in this study were part of a longitudinal dementia research program at the Memory and Aging Center, University of California, San Francisco. Both had an 8-year history of behavioral changes and memory impairment at the time of tissue collection for this study. One patient, a 67-year-old male with sporadic FTD, tested negative for mutations in GRN, MAPT, and C9ORF72. The other patient, a 64-year-old male with a significant family history of dementia, had behavioral variant FTD. MRI in this patient demonstrated severe bifrontal and temporal atrophy associated with gliosis in the frontal lobes (greater on the right). One year later, MRI scans showed progression of atrophy and gliosis. Genetic testing revealed a novel nonsense mutation in GRN, p.S116X (g.4627C > A, c.347C > A), which is predicted to result in a premature stop codon. Both FTD patients had parkinsonism, which is typical of all FTD patients with PGRN mutations. An age-matched subject, a clinically normal 64-year-old male with no mutations in GRN, MAPT, or C9ORF72, served as a control.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "FTD patients", "measured_property": null }, { "quantity": "8-year", "unit": "year", "measured_entity": "two FTD patients", "measured_property": "history of behavioral changes and memory impairment" }, { "quantity": "67-year-", "unit": "year", "measured_entity": "patient", "measured_property": "old" }, { "quantity": "64-year-", "unit": "year", "measured_entity": "other patient", "measured_property": "old" }, { "quantity": "One year", "unit": "year", "measured_entity": "MRI scans", "measured_property": "showed progression of atrophy and gliosis" }, { "quantity": "64-year", "unit": "year", "measured_entity": "age-matched subject", "measured_property": "old" } ], "split": "train", "docId": "S2211124712002884-903", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "To assess the in vivo properties of hESC-derived trigeminal placode precursors, PIP-induced neuronal clusters, derived from a constitutively GFP-positive hESC line (Figures S5A and S5B), were injected into the developing chick embryo targeting the early trigeminal anlage at H&H stage 10\u201312 (Figure S5C). Human cells were identified based on GFP expression and use of human specific antibodies against cytoplasmic antigen (hCA). Two days after in ovo transplantation, surviving GFP+ cells were found dispersed in the area of the endogenous chick trigeminal ganglion (Figure 4P). We observed extensive GFP+ human fiber bundles coexpressing hCA and peripherin (Figures 4Q and 4R). In contrast, no hCA or peripherin expression was detected in the neural tube of the embryo (Figure S5D). The in vivo fiber outgrowth 2 days after transplantation was reminiscent of the extensive in vitro fiber outgrowth of replated trigeminal neuron clusters (Figure S5A). Peripherin expression in vivo (Figure 4S) confirmed the peripheral neuron identity of the grafted cells.", "measurement_extractions": [ { "quantity": "Two days", "unit": "days", "measured_entity": "surviving GFP+ cells", "measured_property": "dispersed" }, { "quantity": "2 days", "unit": "days", "measured_entity": "in vivo fiber outgrowth", "measured_property": null } ], "split": "train", "docId": "S2211124713006475-1195", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We next addressed whether hESC-derived trigeminal neurons can engraft in the adult mouse CNS and project toward their physiological target. The trigeminal nuclei in the brainstem receive afferent innervation from the trigeminal sensory ganglion that is relayed to the contralateral thalamus. The pons was selected as site for transplantation, because it is surgically accessible and located within proximity of the trigeminal brain stem nuclei that receive afferent input from the trigeminal ganglia. Hence, GFP+ human trigeminal neuron clusters were injected into adult NOD/SCID mice via stereotactic surgery (see Experimental Procedures). Histological analysis 4 weeks after transplantation showed survival of GFP+ human cell graft in the ventral pons (Figure S5E). Although GFP+ cell bodies remained tightly clustered at injection site, GFP+ fibers showed extensive projections into the host brain (n = 6) including the endogenous trigeminal nuclei (Figure S5F). Expression of BRN3A confirmed the sensory neuron identity of the cells (Figure S5G). Graft-derived human fiber bundles (hNCAM+ and GFP+) were observed emanating from the graft core (Figure S5H). These data demonstrate in vivo survival of trigeminal placode derivatives, differentiation along sensory neuron lineage, and the establishment of axonal projections toward relevant endogenous targets in the embryonic chick and adult mouse brain.", "measurement_extractions": [ { "quantity": "4 weeks", "unit": "weeks", "measured_entity": "Histological analysis", "measured_property": null }, { "quantity": "6", "unit": null, "measured_entity": "GFP+ fibers showed extensive projections into the host brain", "measured_property": "n" } ], "split": "train", "docId": "S2211124713006475-1205", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(O) GH plasma levels using a human specific ELISA (6 weeks after transplantation).", "measurement_extractions": [ { "quantity": "6 weeks", "unit": "weeks", "measured_entity": "GH plasma levels", "measured_property": "after transplantation" } ], "split": "train", "docId": "S2211124713006475-841", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The second feature of interest in the network change is that it is observed in the early stages of auditory processing. The MMNm component is an early (between 100 and 200 ms), automatic or largely pre-attentive response (Naatanen et al., 2007). Particularly relevant is that a prefrontal contribution to change detection also occurs within this early time window, demonstrated in Garrido and colleagues' dynamic causal modelling of a roving oddball paradigm (Garrido et al., 2008) and the effects of frontal lesions (Alho et al., 1994). This suggests that if the prefrontal cortical dysfunction in the two patient groups is the cause of the altered network response, then this affects the very early stages of stimuli processing. Although this early component is distinct from the later M300 response, which reflects attentional processing (Wronka et al., 2008), it is still relevant to high order cognitive processes, (Naatanen et al., 2007, 2012).", "measurement_extractions": [ { "quantity": "between 100 and 200 ms", "unit": "ms", "measured_entity": "MMNm component", "measured_property": null } ], "split": "train", "docId": "S2213158213000302-1597", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For group analyses, the following procedures were applied. (1) T1-weighted sMRIs from each subject (Fig. 1A) were registered to MNI space (Montreal Neurological Institute, MNI-152 atlas as in Fig. 1B) using an affine transformation (FLIRT\u2013FMRIB's Linear Image Registration Tool) (Jenkinson and Smith, 2001) in FSL (www.fmrib.ox.ac.uk/fsl/). (2) The cortical (Fig. 1C) and subcortical masks with pre-defined brain regions from the standard atlas were transferred to the individual's headspace (Fig. 1D), using the inverse of the transformation obtained in the first step: the Harvard-Oxford Atlas, part of the FSL software with masks of 96 cortical gray-matter regions (48 regions in each hemisphere), 21 sub-cortical regions, and cerebellum, was used. (3) The regional masks were down-sampled to a cubic source grid with voxels of 5 mm per side (Fig. 1E). (4) VESTAL MEG source imaging used the source grid from step 3. This step permits group-based analyses. In the shown example, MEG responses evoked by S1 localized to left and right Heschl's gyri (Fig. 1F). (5) Finally, for regions of interest (ROIs), the source time course was obtained by summing activity from all ROI voxels. Fig. 1H shows the time course from left Heschl's gyrus (dark blue region in Fig. 1C and D).", "measurement_extractions": [ { "quantity": "96", "unit": null, "measured_entity": "cortical gray-matter regions", "measured_property": null }, { "quantity": "48", "unit": null, "measured_entity": "regions", "measured_property": null }, { "quantity": "21", "unit": null, "measured_entity": "sub-cortical regions", "measured_property": null }, { "quantity": "5 mm per side", "unit": "mm per side", "measured_entity": "cubic source grid", "measured_property": "voxels" } ], "split": "train", "docId": "S2213158213000582-1327", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Prior to VESTAL analyses, a 5\u201355 Hz bandpass filter was applied. VESTAL analyses examined activity 30\u2013130 ms post-stimulus producing a 4D activation map (3D volumes across time) as well as a 2D source time-course matrix. The average percent variance explained for gradiometer data using VESTAL was 95.81% for HC and 94.38% for SZ. The average percent variance explained for magnetometer data using VESTAL program was 96.24% for HC and 93.17% for SZ. There were no group differences in percent variance explained for gradiometer data (t(39) = 1.16, p = 0.25) or magnetometer data (t(39) = 1.31, p = 0.20).", "measurement_extractions": [ { "quantity": "5\u201355 Hz", "unit": "Hz", "measured_entity": "bandpass filter", "measured_property": null }, { "quantity": "30\u2013130 ms", "unit": "ms", "measured_entity": "VESTAL analyses", "measured_property": "examined activity" }, { "quantity": "95.81%", "unit": "%", "measured_entity": "gradiometer data using VESTAL", "measured_property": "average percent variance explained" }, { "quantity": "94.38%", "unit": "%", "measured_entity": "gradiometer data using VESTAL", "measured_property": "average percent variance explained" }, { "quantity": "96.24%", "unit": "%", "measured_entity": "magnetometer data using VESTAL", "measured_property": "average percent variance explained" }, { "quantity": "93.17%", "unit": "%", "measured_entity": "magnetometer data using VESTAL", "measured_property": "average percent variance explained" }, { "quantity": "1.16", "unit": null, "measured_entity": "gradiometer data", "measured_property": "t(39)" }, { "quantity": "0.25", "unit": null, "measured_entity": "gradiometer data", "measured_property": "p" }, { "quantity": "1.31", "unit": null, "measured_entity": "magnetometer data", "measured_property": "t(39)" }, { "quantity": ".20", "unit": null, "measured_entity": "magnetometer data", "measured_property": "p" } ], "split": "train", "docId": "S2213158213000582-1340", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(I\u2013M) Teratoma formation at 3 months after transplantation in the testes of SCID mice. H&E staining of the sections showed histological features of the neuroepithelium (J), cartilage (K), muscle (L) and gut-like epithelium (M).", "measurement_extractions": [ { "quantity": "3 months", "unit": "months", "measured_entity": "Teratoma formation", "measured_property": "after transplantation" } ], "split": "train", "docId": "S2213671113000738-430", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(R) Quantification of immunocytochemical analyses for each iPSC line. Data are shown as the means \u00b1 SD (n = 3 independent experiments). SER, serotonin, TUB\u03b2III, \u03b2-tubulin class III. Scale bars: 200 \u03bcm in (A)\u2013(H), 50 \u03bcm in insets of (C)\u2013(F), 100 \u03bcm in (J)\u2013(M) and (Q).", "measurement_extractions": [ { "quantity": "3", "unit": null, "measured_entity": "Data", "measured_property": "n" }, { "quantity": "200 \u03bcm", "unit": "\u03bcm", "measured_entity": "(A)\u2013(H)", "measured_property": "Scale bars" }, { "quantity": "50 \u03bcm", "unit": "\u03bcm", "measured_entity": "(C)\u2013(F)", "measured_property": "Scale bars" }, { "quantity": "100 \u03bcm", "unit": "\u03bcm", "measured_entity": "(J)\u2013(M) and (Q)", "measured_property": "Scale bars" } ], "split": "train", "docId": "S2213671113000738-435", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(A) Flow-cytometric analyses for MHC-I (HLA-A, HLA-B, and HLA-C). Incubation of the cells with IFN-\u03b3 for 48 hr increased the MHC-I expression (green).", "measurement_extractions": [ { "quantity": "48 hr", "unit": "hr", "measured_entity": "cells", "measured_property": "Incubation" } ], "split": "train", "docId": "S2213671113000738-445", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(Q\u2013S) Magnetic resonance images of a representative animal (No. 6, autograft) at 3 months after the transplant. The arrowheads indicate the directions of the cell injections. (Q) coronal, (R) axial, and (S) sagittal. The letter L indicates the left side.", "measurement_extractions": [ { "quantity": "3 months", "unit": "months", "measured_entity": "Magnetic resonance images of a representative animal", "measured_property": null } ], "split": "train", "docId": "S2213671113000738-485", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For the first two animals (Nos. 1 and 4), we established iPSCs from fibroblasts derived from the oral mucosa using retroviral vectors (Okita et al., 2011). For the other two animals (Nos. 6 and 8), we used peripheral blood mononuclear cells (PBMCs) with nonintegrating episomal vectors (Okita et al., 2013). We selected the best clone from each animal according to the following criteria: a stable embryonic stem cell (ESC)-like morphology of the colonies after passaging, expression of pluripotent markers, few or no integrated transgenes (Figures 1A\u20131F; Figure S1 available online), and the potential for stable neural differentiation. A PCR analysis revealed that all of the clones with retroviral vectors showed apparent expression of remaining transgenes (Figures S1C and S1D), whereas the clones with episomal vectors never did (Figure S1F). To detect the iPSC-derived cells in a brain, we introduced GFP (Figures 1G and 1H). The selected clones of iPSCs had the potential to generate teratomas in the testes of a severe combined immunodeficiency (SCID) mouse within 12 weeks (Figures 1I\u20131M).", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "animals", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "animals", "measured_property": null }, { "quantity": "within 12 weeks", "unit": "weeks", "measured_entity": "testes of a severe combined immunodeficiency (SCID) mouse", "measured_property": "generate teratomas" } ], "split": "train", "docId": "S2213671113000738-647", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In sequential PET studies, we observed increased uptake of [11C]PK11195 in one allograft (animal No. 10) at 3 months (Figures 3A and 3B). We could not detect any apparent uptake in the other animals or at any other time points (Figure S2). Intriguingly, the serum level of IFN-\u03b3 temporarily increased at 2 months after the transplant in three animals (Figure 3C). An immunofluorescence study conducted at 3.5\u20134 months showed that MHC-II+ cells were more frequently found in allografts than in autografts, especially in the monkey with increased uptake of [11C]PK11195 (Figure 3D, No. 10). The MHC-II staining never overlapped with that of GFP of the donor cells (Figure 3F), whereas it generally overlapped with that of IBA1 (Figure 3G), indicating that MHC-II was expressed by host-derived microglia. Consistently, the number and density of IBA1+ cells were higher in allografts than in autografts (Figures 3E, 3H, and S4C). An increase in the expression of MHC might trigger the recruitment of circulating immune cells, including T cells. An immunofluorescence study revealed that more CD45+ cells (a marker for pan-leukocytes) accumulated in allografts compared with autografts (Figures 3I and 3J). Most of the CD45+ cells were CD3+ T cells, and 60% of them were CD8+ killer T cells (Figures 3K and 3L). These findings suggest that an acquired immune response was elicited only in the allografts in the primate brain.", "measurement_extractions": [ { "quantity": "one", "unit": null, "measured_entity": "allograft", "measured_property": null }, { "quantity": "3 months", "unit": "months", "measured_entity": "observed", "measured_property": null }, { "quantity": "2 months", "unit": "months", "measured_entity": "serum level of IFN-\u03b3 temporarily increased", "measured_property": null }, { "quantity": "three", "unit": null, "measured_entity": "animals", "measured_property": null }, { "quantity": "3.5\u20134 months", "unit": "months", "measured_entity": "immunofluorescence study conducted", "measured_property": null }, { "quantity": "60%", "unit": "%", "measured_entity": "CD45+ cells", "measured_property": "CD8+ killer T cells" } ], "split": "train", "docId": "S2213671113000738-684", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Another important finding is that, in spite of the immune responses mounted by the host brain, a substantial number of TH+ cells survived in the allografts. This is consistent with previous clinical reports of human fetal cell transplantation. Postmortem analyses of the patients revealed robust survival of DA neurons in spite of the fact that numerous immune cells were present around the graft (Kordower et al., 1997). In two double-blind clinical trials, immunosuppressive drugs were never used (Freed et al., 2001) or were withdrawn after 6 months (Olanow et al., 2003). In these cases, the cells from multiple fetuses were used without HLA matching, but more than 50,000 TH+ cells had survived after several years. Our quantitative PCR (qPCR) study in vitro showed that the expression of MHC-I increased in response to IFN-\u03b3, but the expression level was still 1/10 that of untreated monkey peripheral blood cells (Figure 2B). The in vivo studies revealed that the serum level of IFN-\u03b3 increased at 2 months, and CD45+ cells (including CD8+ cells) accumulated in the allografts 3.5\u20134 months after the transplant. On the other hand, the levels of INF-\u03b3 in the cerebrospinal fluid (CSF) and the levels of tumor necrosis factor \u03b1 (TNF-\u03b1) in both the serum and CSF were below the limit of detection by ELISA (data not shown). An immunofluorescence study did not reveal any apparent expression of MHC-I by the grafted cells (Figure S4A). Therefore, it is possible that the immune response in the primate brain was not strong enough to reject all of the donor cells. These findings closely correlate with the results of previous murine experiments (Hudson et al., 1994; Shinoda et al., 1995). To apply our findings to a more clinically relevant setting, we investigated the expression of HLA-I during neural differentiation of human ESCs (hESCs) and iPSCs by qPCR (Figure S4D). The expression level was 1/100 compared with that of human peripheral blood cells in both hESCs and iPSCs, and it was similarly elevated in response to IFN-\u03b3. It is difficult to precisely compare immunogenicity in monkeys with that in humans, but the low expression level of MHC-I by the donor cells may account for the mild rejection in both monkey and human neural transplantation.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "double-blind clinical trials", "measured_property": null }, { "quantity": "6 months", "unit": null, "measured_entity": null, "measured_property": "withdrawn" }, { "quantity": "1/10", "unit": null, "measured_entity": "untreated monkey peripheral blood cells", "measured_property": "expression of MHC-I" }, { "quantity": "2 months", "unit": "months", "measured_entity": "serum level of IFN-\u03b3 increased", "measured_property": null }, { "quantity": "3.5\u20134 months", "unit": "months", "measured_entity": "CD45+ cells", "measured_property": "accumulated in the allografts" }, { "quantity": "1/100", "unit": null, "measured_entity": "human peripheral blood cells in both hESCs and iPSCs", "measured_property": "expression of HLA-I during neural differentiation of human ESCs (hESCs) and iPSCs" } ], "split": "train", "docId": "S2213671113000738-738", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(C) C-peptide and PDX1 expression was confirmed by immunocytochemistry of cells differentiated for 25 days.", "measurement_extractions": [ { "quantity": "25 days", "unit": "days", "measured_entity": "cells", "measured_property": "differentiated" } ], "split": "train", "docId": "S2213671113000908-640", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Our group has developed a defined culture system to direct the differentiation of hPSCs into a near-homogenous population of definitive endoderm (DE) cells that have the capacity to differentiate into hepatocytes and pancreatic progenitors (Brown et al., 2011; Cho et al., 2012; Rashid et al., 2010; Touboul et al., 2010; Vallier et al., 2009a; Yusa et al., 2011). Cells grown in these culture conditions successively express primitive streak markers (T and Mixl1), downregulate pluripotency markers (NANOG, SOX2, and POU5F1) and progressively upregulate definitive endoderm markers (CXCR4, FOXA2, GATA4, CERB, and SOX17) (Figures S1A\u2013S1C available online). Flow cytometry analyses showed that 80% of the resulting DE population coexpresses CXCR4 and SOX17 (Figure S1D). Interestingly, the resulting population of DE cells is negative for genes marking the foregut (SOX2), the midgut/hindgut (CDX2), the pancreas (PDX1), the liver (AFP), and the lungs (HOXA1) (Figures S1E and S1F) This confirms that DE cells generated in vitro could correspond to early endoderm progenitor cells prior to anteroposterior patterning or organogenesis.", "measurement_extractions": [ { "quantity": "80%", "unit": "%", "measured_entity": "resulting DE population", "measured_property": "coexpresses CXCR4 and SOX17" } ], "split": "train", "docId": "S2213671113000908-810", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "GFP-expressing H9, BBHX8, and A1ATD-1 cells were generated by stable transfection using lipofectamine 2000 (Invitrogen) as described previously (Vallier et al., 2001). GFP-positive cells were differentiated into foregut cells and then dissociated into single cells. An individually isolated GFP cell was then transferred into a well containing non-GFP-positive hFSCs. Wells were visually inspected 12 hr after plating, and wells containing a single GFP-positive hFSC were selected for clonal expansion.", "measurement_extractions": [ { "quantity": "12 hr", "unit": "hr", "measured_entity": "Wells", "measured_property": "visually inspected" } ], "split": "train", "docId": "S2213671113000908-979", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Based on several previous publications (Khan et al., 2010, 2011), we explored the utility of adeno-associated virus (AAV) as a method to improve gene targeting efficiencies in hPSCs. For SOX2, a gene that is highly expressed in undifferentiated hPSCs, gene targeting rates were greater than 70%. Similar targeting efficiencies in hPSCs using AAV have been reported by others (Asuri et al., 2012; Khan et al., 2010, 2011; Smith-Arica et al., 2003), indicating that AAV offers a highly efficient and robust approach to target genes for HR in hPSCs.", "measurement_extractions": [ { "quantity": "greater than 70%", "unit": "%", "measured_entity": "SOX2", "measured_property": "gene targeting rates" } ], "split": "train", "docId": "S2213671113000921-1279", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(D) IF analysis of GFP and SOX2 showed colocalization in NPCs (scale bar represents 100 \u03bcm).", "measurement_extractions": [ { "quantity": "100 \u03bcm", "unit": "\u03bcm", "measured_entity": "scale bar", "measured_property": null } ], "split": "train", "docId": "S2213671113000921-714", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Using a recombinant adeno-associated viral (rAAV)-based gene-targeting method, we inserted the gene-encoding GFP into the SOX2 locus in H9 hESCs (Figure 1A). Proper homologous recombination led to the replacement of the SOX2 open reading frame with that of GFP and a neomycin selection cassette (SV40-Neo). After infection with rAAV and G418 drug selection, a total of 36 clones were expanded and screened by Southern blotting for homologous recombination events. Among these clones, 26 (72%) were found to carry the GFP-Neo cassette in the SOX2 locus (Figure S1A available online). No clones in which both SOX2 alleles were disrupted were isolated. Our subsequent analysis focused on one of these clones, clone 23 (hSOX2-23). We confirmed appropriate gene targeting in this clone using multiple restriction digests followed by Southern blotting (Figures 1B, S1B, and S1C). We did not observe nontargeted insertions of the rAAV sequences, and cells exhibited a normal karyotype (data not shown). Flow cytometry of hSOX2-23 revealed that the majority of the cells expressed GFP (Figure 1C). By comparison, a drug-selected clone, hSOX2-25, which was negative for targeted insertion (Figure S1A), showed no detectable GFP (Figure S2A). Despite only having one copy of SOX2, hSOX2-23 had similar levels of SOX2, OCT4, and NANOG expression as hSOX2-25 and wild-type (WT) hESCs (Figure S2B). Moreover, the percentage of GFP-positive (GFP+) cells in hSOX2-23 was constant over more than 20 passages. Immunofluorescence (IF) staining of hSOX2-23 showed that 100% of GFP+ cells expressed SOX2 protein (Figure S2C). Additionally, hSOX2-23 colonies had characteristic hESC morphology (Figure S2D) and expressed markers of the undifferentiated state, such as NANOG (Figure S2E). These results show that this rAAV-based gene-targeting method can be used to efficiently disrupt genes by homologous recombination. In addition, the SOX2-GFP hESC marker line can be used to monitor SOX2 expression in undifferentiated hESCs.", "measurement_extractions": [ { "quantity": "36 clones", "unit": "clones", "measured_entity": "expanded and screened by Southern blotting for homologous recombination events", "measured_property": null }, { "quantity": "26", "unit": null, "measured_entity": "clones", "measured_property": null }, { "quantity": "72%", "unit": "%", "measured_entity": "clones", "measured_property": "found to carry the GFP-Neo cassette in the SOX2 locus" }, { "quantity": "one", "unit": null, "measured_entity": "clones", "measured_property": null }, { "quantity": "over more than 20 passages.", "unit": "passages", "measured_entity": "hSOX2-23", "measured_property": "percentage of GFP-positive (GFP+) cells" }, { "quantity": "100%", "unit": "%", "measured_entity": "GFP+ cells", "measured_property": "expressed SOX2 protein" } ], "split": "train", "docId": "S2213671113000921-994", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "\u2022Adult hRPESC-derived RPE had comparable in vitro characteristics to fetal hRPE\u2022hRPE monolayers survived 4 weeks on PET carriers under the rabbit retina\u2022Better xenograft survival may be due to the maintained hRPE cell polarity\u2022Atrophy of the retina overlaying the hRPE xenograft remains a future challenge", "measurement_extractions": [ { "quantity": "4 weeks", "unit": "weeks", "measured_entity": "hRPE monolayers", "measured_property": "survived" } ], "split": "train", "docId": "S2213671113001306-1286", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We asked whether encapsulating the graft in additional materials would help the surgical delivery and potentially reduce the retinal thinning. Temporary graft encapsulation with thermosensitive gelatin and/or plasmin-assisted vitrectomy, however, was found to cause more aggressive retinal destruction and choroidal engorgement on SD-OCT compared to unaided implantation of fetal hRPE/PET implants (Figure S2). In contrast, we found that all these negative effects were ameliorated through 1 to 2 mg intravitreal triamcinolone (TCA) injection at the end of the surgery. This long-acting synthetic corticosteroid is routinely given intraocularly to reduce immune responses. Ophthalmic and systemic complications related to the implantation procedure are summarized in Table 2.", "measurement_extractions": [ { "quantity": "1 to 2 mg", "unit": "mg", "measured_entity": "intravitreal triamcinolone (TCA) injection", "measured_property": null } ], "split": "train", "docId": "S2213671113001306-1398", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Animals were sacrificed and perfusion-fixed at 4 weeks posttransplantation and the recovered grafts then sectioned. Microscopic inspection revealed that the grafts had been maintained as a largely intact and continuous cell monolayer. A positive pan-cytokeratin (pCK) signal, an established RPE marker, was confirmed for fetal and adult hRPE monolayers (Figures 3A and 3C). Additional pCK reactivity was seen \u201cunderneath\u201d the cell carrier, likely from host RPE. Moreover, fetal and adult hRPE on PET carriers stained positively for the human-specific marker SC121, confirming survival of human RPE for 1 month as a monolayer (Figure 3). Costaining of SC121 with an antibody to MCT1 and Ezrin, both apical membrane markers, further confirmed that the RPE were (still) polarized (Figure 3). SC121+ RPE transplants were negative for the expression of the cell-cycle marker ki67, the proliferation marker phosphohistone H3, and for the apoptotic marker caspase-3 (Figures 3F\u20133H), indicating absence of proliferation and apoptosis. We estimated the total human RPE cell survival to be approximately 95% after 1 month by using the SC121 positivity; we measured the total length of the carrier and the length of SC121 stain and calculated the percent coverage of SC121 over the total carrier length. On transmission electron micrography (TEM), polarized RPE cells were observed on the PET carriers from both fetal and adult transplants (Figures 3I and 3J). These results confirm survival of polarized human RPE from fetal and adult donors xenografted into rabbit SRS over 4 weeks.", "measurement_extractions": [ { "quantity": "4 weeks", "unit": "weeks", "measured_entity": "Animals", "measured_property": "sacrificed and perfusion-fixed" }, { "quantity": "1 month", "unit": "month", "measured_entity": "human RPE", "measured_property": "survival" }, { "quantity": "approximately 95%", "unit": "%", "measured_entity": "human RPE cell", "measured_property": "survival" }, { "quantity": "after 1 month", "unit": "month", "measured_entity": "human RPE cell", "measured_property": "survival" }, { "quantity": "over 4 weeks", "unit": "weeks", "measured_entity": "polarized human RPE", "measured_property": "survival" } ], "split": "train", "docId": "S2213671113001306-1404", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The Spectralis HRA/OCT device (Heidelberg Engineering) was used to acquire laser-interferometric reflectance OCT images of the retina and choroid, with resolutions comparable to a light microscopic section. Longitudinal and transversal line OCT scans through the center of the implant were taken with the 30-degrees-of-visual-field setting of the Spectralis. Volume scans were obtained with 60 \u03bcm distance between each scan with the device set to 20 \u00d7 20 degrees of visual field centered on the implant. To approximate human optical parameters to rabbit eyes, the Spectralis\u2019 corneal curvature settings were set by default to 4.2 mm. Red-free and infrared cSLO images were taken at 30 degrees of visual field in the HRA mode of the device.", "measurement_extractions": [ { "quantity": "30-degrees", "unit": "degrees", "measured_entity": "Spectralis", "measured_property": "visual-field setting" }, { "quantity": "60 \u03bcm", "unit": "\u03bcm", "measured_entity": "scan", "measured_property": "distance between" }, { "quantity": "20 \u00d7 20 degrees", "unit": "degrees", "measured_entity": "device", "measured_property": "visual field" }, { "quantity": "4.2 mm", "unit": "mm", "measured_entity": "Spectralis\u2019", "measured_property": "corneal curvature settings" }, { "quantity": "30 degrees", "unit": null, "measured_entity": "device", "measured_property": "visual field" } ], "split": "train", "docId": "S2213671113001306-1520", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(F) Adult hRPE cultures display expression of markers typical of native RPE in their polarized localization. DAPI is cyan, whereas all other immunofluorescence is gold. Claudin 19, ezrin, ZO1, and MCT1 are preferentially located on the apical side. RPE65 and CRALBP are cytoplasmic. The scale bars represent 10 \u03bcm.", "measurement_extractions": [ { "quantity": "10 \u03bcm", "unit": "\u03bcm", "measured_entity": "scale bars", "measured_property": null } ], "split": "train", "docId": "S2213671113001306-885", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(A) Fetal hRPE stained for pan-cytokeratin (scale bar, 50 \u03bcm); inset shows section overview stained with hematoxylin/eosin (scale bar, 200 \u03bcm).", "measurement_extractions": [ { "quantity": "50 \u03bcm", "unit": "\u03bcm", "measured_entity": "scale bar", "measured_property": null }, { "quantity": "200 \u03bcm", "unit": "\u03bcm", "measured_entity": "scale bar", "measured_property": null } ], "split": "train", "docId": "S2213671113001306-907", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(B) Fetal hRPE stained for SC121 (red) and MCT1 (green; scale bars, 125 \u03bcm and 25 \u03bcm [inset]).", "measurement_extractions": [ { "quantity": "125 \u03bcm", "unit": "\u03bcm", "measured_entity": "scale bars", "measured_property": null }, { "quantity": "25 \u03bcm", "unit": "\u03bcm", "measured_entity": "inset", "measured_property": null } ], "split": "train", "docId": "S2213671113001306-908", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(D\u2013H) Adult hRPE stained for SC121 (red). (D) Adult hRPE stained for MCT1 (green). (E) Human adult RPE stained for ezrin (green). hRPEs transplanted into rabbit SRS show absence of expression of ki67 (F), phosphohistone H3 (G), and caspase-3 (H). Polarized fetal and adult hRPE cells were found in TEM (I and J). Nuclei with regular chromatin were found in the basal compartment, a basal lamina ([I], large black arrowhead) had formed between the xenograft and PET carrier (black asterisks). Melanosomes (M) in multiple stages, some microvilli abutting to the atrophic neural retina (NR), and junctional structures with desmosomes (small black arrowhead) and tight junctions (red arrowhead) were discerned apically. Mitochondria (MC) were seen in the basolateral part of the cell. Detachment from cell carrier (asterisk) in (J) is a histologic processing artifact. Left images in (I) and (J) taken at 10,500\u00d7 magnification; right micrographs are rectangular zone in left at 25,000\u00d7; scale bars represent 2 \u03bcm/inset 0.2 \u03bcm distance in (I) and (J).", "measurement_extractions": [ { "quantity": "10,500\u00d7", "unit": "\u00d7", "measured_entity": "Left images", "measured_property": "magnification" }, { "quantity": "25,000\u00d7", "unit": "\u00d7", "measured_entity": "right micrographs", "measured_property": "magnification" }, { "quantity": "2 \u03bcm", "unit": "\u03bcm", "measured_entity": "scale bars", "measured_property": "distance" }, { "quantity": "0.2 \u03bcm", "unit": "\u03bcm", "measured_entity": "scale bars", "measured_property": "distance" } ], "split": "train", "docId": "S2213671113001306-910", "dataset": "measeval" } ]