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[
{
"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": "Correspondence analysis (CA) and statistical diversity analysis were carried out on the palynological dataset (total counts per gram) to confirm assemblage designations (Figs. 4 and 5), to identify any disturbance to the core prior to interpretation, and to estimate diversity (Fig. 6). Dinoflagellate cyst assemblages (DA1\u2013DA5) and pollen assemblages (PA1\u2013PA4) were defined by visually comparing changes in the species dominance (Figs. 7 and 8), and confirmed by CA (Fig. 4) using the first three axes (describing the highest percentages of variance). Five samples from below the CIE at 2619.60, 2617.35, 2617.44, 2614.73, and 2614.71 m (indicated in Fig. 5) contain Apectodinium, in contrast to the other samples below the CIE (Figs. 3 and 7). Some of these samples (2619.60, 2614.73, and 2614.71 m) also contain negative \u03b413CTOC, indicative of the CIE (Fig. 3). To test if coincident pollen and spore changes also occur in these samples, we used CA on the pollen and spore data only (Fig. 5a). PA1\u2013PA4 (symbols) plot in clusters, signifying their palynological similarity. The species most associated with an assemblage are clustered with the samples from that assemblage. For example, Inaperturopollenites hiatus and bisaccate pollen (highly abundant before the CIE, Fig. 8) are high on axis 1 where the earlier samples from PA1 and PA2 occur, and Caryapollenites spp. and fungal spores (abundant after the CIE, Fig. 8) are low on axis 1 near the younger samples from PA4. The two samples 2619.60 and 2614.71 have a spore and pollen palynological signature similar to samples from PA3/4 during the CIE (plot lower on axis 1) and are either not in the correct location (it cannot be discounted that these samples represent tectonically emplaced younger material (Payne et al., 2005), or were misplaced during drilling operations core handling), or represent very short episodes of both marine and terrestrial ecologic change to CIE-type conditions. The rapid and transient nature of these two shifts appears to suggest that the latter explanation may be unlikely, and we have therefore shaded samples from these two depths in Figs. 3, 6\u20139.",
"measurement_extractions": [
{
"quantity": "Five",
"unit": null,
"measured_entity": "samples",
"measured_property": null
},
{
"quantity": "2619.60, 2617.35, 2617.44, 2614.73, and 2614.71 m",
"unit": "m",
"measured_entity": "samples",
"measured_property": null
},
{
"quantity": "2619.60, 2614.73, and 2614.71 m",
"unit": "m",
"measured_entity": "samples",
"measured_property": null
},
{
"quantity": "two",
"unit": null,
"measured_entity": "samples",
"measured_property": null
},
{
"quantity": "2619.60 and 2614.71",
"unit": null,
"measured_entity": "samples",
"measured_property": null
}
],
"split": "val",
"docId": "S0012821X12004384-1302",
"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": "Dinoflagellate cysts have been used extensively for reconstructing paleoenvironments in the Paleogene (see overview in Sluijs et al., 2005), as they are particularly sensitive to changes in salinity, temperature, and nutrient levels (Powell et al., 1992; Pross and Brinkhuis 2005; Sluijs et al., 2005). We calculate \u201c%low salinity dinoflagellate cysts\u201d (Figs. 7\u20139) by grouping cysts of similar inferred ecologic preferences (see Fig. 7, and discussion in the Supplementary Material) to provide an indication of environmental change, and by excluding species of uncertain affinity such as Apectodinium. Samples with fewer than 20 specimens were also excluded. Despite the limitations of this method, the large variation in the %low salinity dinoflagellate cysts (ranging from 0% to 80%) clearly indicates that significant environmental changes in surface water conditions occurred during the CIE onset in the central North Sea, and is supported by coeval changes in the sedimentary carbon/nitrogen (C/N) ratio (Fig. 10) which reflects changes in the proportion of terrestrial/marine organic material deposited in the North Sea Basin due to terrestrial runoff and productivity (see Section 4.4).",
"measurement_extractions": [
{
"quantity": "20",
"unit": null,
"measured_entity": "specimens",
"measured_property": null
},
{
"quantity": "0% to 80%",
"unit": "%",
"measured_entity": "Samples",
"measured_property": "%low salinity dinoflagellate cysts"
}
],
"split": "val",
"docId": "S0012821X12004384-1405",
"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": "Dinoflagellate cyst assemblage 1 (DA1, from 2632 to 2618 m, Fig. 7p) contains high proportions of typically open marine and hence normal marine salinity associated Achomosphaera/Spiniferites spp. , undifferentiated chorate cysts and Areoligera/Glaphyrocysta spp. DA1 also contains on average 5% peridinoid cysts including Deflandrea spp. regarded as a coastal/neritic taxon indicating high productivity and nutrient availability (Brinkhuis, 1994; Pross and Brinkhuis, 2005). These characteristics indicate that a somewhat restricted but fully marine shelf environment was present before the onset of the CIE in the central North Sea, with availability of nutrients indicated by the presence of Deflandrea spp.",
"measurement_extractions": [
{
"quantity": "from 2632 to 2618 m",
"unit": "m",
"measured_entity": "Dinoflagellate cyst assemblage 1 (DA1",
"measured_property": null
},
{
"quantity": "on average 5%",
"unit": "%",
"measured_entity": "DA1",
"measured_property": "peridinoid cysts"
}
],
"split": "val",
"docId": "S0012821X12004384-1415",
"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": "Alternatively, an increase in regional precipitation could have caused elevated terrestrial runoff (C/N ratios, kaolinite) and lower surface water salinity above 2618 m before the CIE onset (Fig. 9). The North Sea Basin surrounding landmasses were within the northern rain belt (the southern boundary today is 40\u00b0N), which would have experienced elevated precipitation if the global hydrologic cycle became enhanced (Pagani et al., 2006b; Schmitz et al., 2001). This scenario would be consistent with a gradual increase in the global hydrologic cycle before the CIE, perhaps from gradual warming, which was hypothesised to have triggered ocean circulation changes, methane hydrate destabilisation, and global carbon release at the CIE (Bice and Marotzke, 2002). We note however that there is currently no evidence for an enhanced hydrologic cycle well before the CIE in other regions.",
"measurement_extractions": [
{
"quantity": "above 2618 m",
"unit": "m",
"measured_entity": "CIE onset",
"measured_property": null
},
{
"quantity": "40\u00b0N",
"unit": "\u00b0N",
"measured_entity": "northern rain belt",
"measured_property": "southern boundary"
}
],
"split": "val",
"docId": "S0012821X12004384-1594",
"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 results provide the first evidence that the North Sea became stratified from 103 yrs before the CIE onset (above 2618 m, Fig. 9). This is significant as Nisbet et al. (2009) hypothesised that the proximal Kilda Basin became stratified and anoxic before the CIE, allowing significant build-up of methane and CO2 at depth. They proposed that overturning of this basin could have released greenhouse gases and triggered the CIE, although there is currently no direct evidence as marine records from the Kilda Basin remain rare (Nisbet et al., 2009). Our North Sea records likely indicate enhanced stratification also of the proximal and linked Kilda Basin before the CIE (Fig. 1). Evidence for the linkage of the North Sea, Kilda and Arctic Basins comes from the coincident onset of A. augustum and laminated sediments at the CIE onset in sections from the North Sea (this study), Spitsbergen (Harding et al., 2011), and Lomonosov Ridge (Sluijs et al., 2006). Although our results evidence a probable stratified Kilda Basin before the CIE, proxies for overturning are now needed to further test the Kilda basin hypothesis.",
"measurement_extractions": [
{
"quantity": "from 103 yrs",
"unit": "yrs",
"measured_entity": "CIE onset",
"measured_property": "before"
},
{
"quantity": "above 2618 m",
"unit": "m",
"measured_entity": "CIE onset",
"measured_property": "before"
}
],
"split": "val",
"docId": "S0012821X12004384-1599",
"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 Eocene\u2013Oligocene Transition (EOT) marks the onset of modern icehouse conditions when continental-scale ice sheets enveloped Antarctica (\u2018Oi1,\u2019 \u223c33.7 Ma, Coxall et al., 2005; Ehrmann and Mackensen, 1992). Oi1 typifies the non-linearity of global climate, with the emplacement of an ice volume between 60% and 100% of that present today on Antarctica over only \u223c400 ka, superimposed on a million-year timescale cooling trend (Coxall et al., 2005; Lear et al., 2008; Zachos et al., 2001). Declining atmospheric pCO2 levels are thought to have driven the accelerated onset of Antarctic glaciation during Oi1, with numerical models suggesting a \u2018threshold\u2019 response of the cryosphere to a long-term lowering of pCO2 (DeConto and Pollard, 2003). Recent reconstruction of pCO2 across the late Eocene and early Oligocene (Pagani et al., 2011) documents a decline that began \u223c2 Ma prior to geological and stable isotope evidence for Antarctic glaciation, supporting this hypothesis. However, the mechanisms responsible for this gradual pCO2 drawdown remain unclear. The biological carbon pump, in particular associated with increased diatom abundance in the Southern Ocean during the late Eocene, represents a potential candidate (Rabosky and Sorhannus, 2009; Salamy and Zachos, 1999; Scher and Martin, 2006).",
"measurement_extractions": [
{
"quantity": "\u223c33.7 Ma",
"unit": "Ma",
"measured_entity": "Oi1",
"measured_property": null
},
{
"quantity": "between 60% and 100%",
"unit": "%",
"measured_entity": "ice volume",
"measured_property": "present today on Antarctica"
},
{
"quantity": "\u223c400 ka",
"unit": "ka",
"measured_entity": "Oi1",
"measured_property": "emplacement of an ice volume"
},
{
"quantity": "million-year",
"unit": "year",
"measured_entity": "timescale",
"measured_property": null
},
{
"quantity": "\u223c2 Ma",
"unit": "Ma",
"measured_entity": "decline",
"measured_property": "began"
}
],
"split": "val",
"docId": "S0012821X13002185-1061",
"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": "Sediments were prepared following existing techniques (Hendry et al., 2010), which were refined slightly to better suit the microseparation technique utilised here as a convenient alternative to sieving. Initial cleaning with H2O2 and HCl was carried out to concentrate biogenic opal. From this pre-cleaned sample the >100 \u03bcm fraction was separated using microseparation (Minoletti et al., 2009) and between 50 and 100 spicules were hand-picked. A range of spicule morphotypes were included, as it has been shown that neither spicule morphology nor species composition creates any consistent offset in \u03b430Si (Hendry et al., 2010, 2011). Spicules were sonicated in reagent grade methanol and dried down in 200 \u03bcL of concentrated HNO3. Sponge \u03b430Si data presented here are generally in agreement with the data from this site produced by De la Rocha (2003), despite different methodologies and specific sampling intervals (Fig. S2). XRD analysis showed sponge spicules from the Eocene/Oligocene interval of this site to be amorphous opal (De la Rocha, 2003).",
"measurement_extractions": [
{
"quantity": ">100 \u03bcm",
"unit": "\u03bcm",
"measured_entity": "fraction",
"measured_property": null
},
{
"quantity": "between 50 and 100",
"unit": null,
"measured_entity": "spicules",
"measured_property": null
},
{
"quantity": "200 \u03bcL",
"unit": "\u03bcL",
"measured_entity": "concentrated HNO3",
"measured_property": null
}
],
"split": "val",
"docId": "S0012821X13002185-1200",
"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": "Cleaned sponge and diatom opal was dissolved via wet alkaline digestion (Cardinal et al., 2007; Ragueneau et al., 2005) in 0.2 M NaOH at 100 \u00b0C for 40 min (diatoms) or up to 1 week (sponge spicules). The samples were acidified to pH\u223c2 with 0.2 M thermally distilled HCl and separated from major ions using cation exchange resin (BioRad AG50W-X12, Georg et al., 2006). Silicon isotope analysis was carried out using a Nu Instruments Nu-Plasma HR multi-collector inductively coupled plasma mass spectrometer run in medium resolution mode (m/\u0394m\u223c3500 at 5% and 95%). Samples were introduced via a self-aspirating PFA microconcentric nebuliser (ESI) in a Cetac Aridus II desolvating unit. Measurements included six to eight standard-sample brackets (brackets where the rate of machine drift outstripped bracketing rate were disregarded), each composed of twenty eight-second integrations. Samples were measured relative to the NIST RM 8546 standard. The external diatomite standard (1.26\u00b10.2\u2030, Reynolds et al., 2007) yielded a mean and 2SD of 1.23\u00b10.25\u2030 (n=104). Error bars in the figures and text are this 2SD external reproducibility unless the internal reproducibility of the standard sample brackets was larger, in which case this is quoted instead.",
"measurement_extractions": [
{
"quantity": "0.2 M",
"unit": "M",
"measured_entity": "wet alkaline digestion",
"measured_property": "NaOH"
},
{
"quantity": "100 \u00b0C",
"unit": "\u00b0C",
"measured_entity": "Cleaned sponge and diatom opal",
"measured_property": "dissolved via wet alkaline digestion"
},
{
"quantity": "40 min",
"unit": "min",
"measured_entity": "diatom opal",
"measured_property": "dissolved via wet alkaline digestion"
},
{
"quantity": "up to 1 week",
"unit": "week",
"measured_entity": "Cleaned sponge",
"measured_property": "dissolved via wet alkaline digestion"
},
{
"quantity": "pH\u223c2",
"unit": "pH",
"measured_entity": "samples",
"measured_property": "acidified"
},
{
"quantity": "0.2 M",
"unit": "M",
"measured_entity": "thermally distilled HCl",
"measured_property": null
},
{
"quantity": "5% and 95%",
"unit": "%",
"measured_entity": "mass spectrometer",
"measured_property": "m/\u0394m\u223c3500"
},
{
"quantity": "six to eight",
"unit": null,
"measured_entity": "standard-sample brackets",
"measured_property": null
},
{
"quantity": "twenty",
"unit": null,
"measured_entity": "integrations",
"measured_property": null
},
{
"quantity": "eight-second",
"unit": "second",
"measured_entity": "integrations",
"measured_property": null
},
{
"quantity": "mean and 2SD of 1.23\u00b10.25\u2030",
"unit": "\u2030",
"measured_entity": "Samples",
"measured_property": null
},
{
"quantity": "104",
"unit": null,
"measured_entity": "Samples",
"measured_property": "n"
},
{
"quantity": "1.26\u00b10.2\u2030",
"unit": "\u2030",
"measured_entity": "external diatomite standard",
"measured_property": null
}
],
"split": "val",
"docId": "S0012821X13002185-1217",
"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 culture experiments (Sutton et al., 2013), it has recently been suggested that the diatom silicon isotope fractionation factor may be species dependent, and in particular that the genus Chaetoceros may have a significantly larger, and the species Fragilariopsis kerguelensis, a significantly smaller 30\u03b5 than the widely accepted value of \u223c\u22121\u2030 (De la Rocha et al., 1997). Our previous Southern Ocean core top study included Antarctic Peninsula sediments containing Chaetoceros sp. and Pacific sector sediments containing F. kerguelensis (Egan et al., 2012). The size fractions analysed had up to 40% differences in the abundance of these diatoms (Fig. 5). However, there is no detectable \u03b430Si offset between size fractions in the core tops TAN127 or 385 (Fig. 5a and b), whilst in KC08A (Fig. 5c), there is a slight negative offset of the 12\u201320 \u03bcm fraction which is opposite to that expected if the abundance of Chaetoceros was the cause (the suggested larger fractionation would act to lower \u03b430Si in the smaller size fractions). Rather, the \u03b430Si values of size fractions between 2 and 20 \u03bcm display a good correlation (r2=0.92, Fig. 2a) with surface silicic acid concentration and, where the water source \u03b430Si and silicic acid concentration are known or can be reasonably estimated, converge on an apparent 30\u03b5 of \u223c\u20131\u2030 (Egan et al., 2012; Fripiat et al., 2011c). This suggests that whilst inter-specific differences in fractionation factor exist in culture, in the natural environment the presence of such species does not yield a large offset in the \u03b430Si signature of the diatom population as a whole. Although a down core effect cannot be completely ruled out, the good agreement between the 2\u201310 \u03bcm and 10\u201320 \u03bcm size fractions in our record from Site 1090 (Fig. 6) and our previous core top study (Figs. 2 and 5) suggest that changes in the species specific fractionation factor are unlikely to be the dominant driver of Site 1090 diatom \u03b430Si variation.",
"measurement_extractions": [
{
"quantity": "\u223c\u22121\u2030",
"unit": "\u2030",
"measured_entity": "diatom silicon isotope fractionation factor",
"measured_property": "30\u03b5"
},
{
"quantity": "40%",
"unit": "%",
"measured_entity": "diatoms",
"measured_property": "differences in the abundance"
},
{
"quantity": "12\u201320 \u03bcm",
"unit": "\u03bcm",
"measured_entity": "fraction",
"measured_property": null
},
{
"quantity": "between 2 and 20 \u03bcm",
"unit": "\u03bcm",
"measured_entity": "size fractions",
"measured_property": null
},
{
"quantity": "0.92",
"unit": null,
"measured_entity": "\u03b430Si values of size fractions between 2 and 20 \u03bcm",
"measured_property": "r2"
},
{
"quantity": "\u223c\u20131\u2030",
"unit": "\u2030",
"measured_entity": "\u03b430Si values of size fractions between 2 and 20 \u03bcm",
"measured_property": "apparent 30\u03b5"
},
{
"quantity": "2\u201310 \u03bcm",
"unit": "\u03bcm",
"measured_entity": "size fractions",
"measured_property": null
},
{
"quantity": "10\u201320 \u03bcm",
"unit": "\u03bcm",
"measured_entity": "size fractions",
"measured_property": null
}
],
"split": "val",
"docId": "S0012821X13002185-1231",
"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": "Modern diatom and sponge \u03b430Si calibrations. (a) Diatom \u03b430Si vs. surface water silicic acid concentration in the Southern Ocean. Diatoms filtered from the top 100 m of the water column during in situ studies (crosses, triangles and stars; Cardinal et al., 2007; Fripiat et al., 2011a, 2011c, ; Varela et al., 2004) and core top diatom opal (circles, Egan et al., 2012). (b) Sponge \u03b430Si vs. ambient silicic acid concentration (Hendry and Robinson, 2012). Sponge spicules from modern sponges (open symbols) and core tops (solid symbols).",
"measurement_extractions": [
{
"quantity": "100 m",
"unit": "m",
"measured_entity": "water column",
"measured_property": null
}
],
"split": "val",
"docId": "S0012821X13002185-835",
"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": "Within the Portland core, the Cenomanian\u2013Turonian Boundary Interval (CTBI) was studied in a 17.7 m-thick section of the Bridge Creek Limestone (\u223c12 m) and Hartland Shale (\u223c12.6 m) Members of the Greenhorn Formation (Cobban and Scott, 1972). These units include organic-rich calcareous shales and rhythmically interbedded couplets of shale and fossiliferous biomicritic limestone. The stratigraphy is also characterized by four bentonite units of 1 to 20 cm that have been regionally correlated (Elder, 1988). Recent sanidine 40Ar/39Ar and zircon 206Pb/238U geochronology integrated with astrochronology constrain the CTB at 93.90\u00b10.15 Ma (Meyers et al., 2012a). The CTBI contains a variety of fossil taxa useful for biostratigraphy (e.g., Gale et al., 1993; Kennedy et al., 2000, 2005; Keller and Pardo, 2004; Keller et al., 2004; Cobban et al., 2006) some of which have intercontinental distributions; however, their transcontinental synchronicity is limited. The dominant foraminifera species spanning the CTBI are Rotalipora cushmani, Whiteinella archaeocretacea and Helvetoglobotruncana helvetica (Eicher and Worstell, 1970). The FO (first occurrence) of the ammonite Watinoceras devonense (Fig. 2; Kennedy et al., 2000) marks the basal Turonian, recorded at the base of bed 86 of the Bridge Creek Limestone (Meyers et al., 2001; bed numbers are based on Cobban and Scott, 1972). The FO of W. devonense coincides with the FO of Mytiloides puebloensis (Kennedy et al., 2000), which can be traced through both Tethyan and Boreal regions (Kennedy et al., 2005).",
"measurement_extractions": [
{
"quantity": "17.7 m",
"unit": "m",
"measured_entity": "section of the Bridge Creek Limestone (\u223c12 m) and Hartland Shale (\u223c12.6 m)",
"measured_property": "thick"
},
{
"quantity": "\u223c12 m",
"unit": "m",
"measured_entity": "Bridge Creek Limestone",
"measured_property": null
},
{
"quantity": "\u223c12.6 m",
"unit": "m",
"measured_entity": "Hartland Shale",
"measured_property": null
},
{
"quantity": "four",
"unit": null,
"measured_entity": "bentonite units",
"measured_property": null
},
{
"quantity": "1 to 20 cm",
"unit": "cm",
"measured_entity": "bentonite units",
"measured_property": null
},
{
"quantity": "93.90\u00b10.15 Ma",
"unit": "Ma",
"measured_entity": "CTB",
"measured_property": null
}
],
"split": "val",
"docId": "S0012821X13007309-1482",
"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 onset of OAE 2 is identified by an abrupt 2\u20133\u2030 VPDB \u03b413Corg positive shift from values of \u223c\u221227\u2030 in the upper Hartland Shale, 4.3 m below the CTB (Fig. 2; Supplementary Material, Table 1a; Sageman et al., 2006). The positive excursion is characteristic of the isotopic response during OAE 2 and, although many localities record increased organic carbon deposition at this level (e.g., Tsikos et al., 2004), sites within the WIS do not. Here the onset is characterized by organic-poor interbedded limestones and shales that are generally bioturbated. Shale interbeds in the upper half of the OAE 2 interval, however, do become enriched in TOC in the WIS. The end of OAE 2 is expressed by a gradual fall in \u03b413Corg back to \u223c\u221227\u2030 (Sageman et al., 2006).",
"measurement_extractions": [
{
"quantity": "2\u20133\u2030",
"unit": "\u2030",
"measured_entity": "VPDB \u03b413Corg",
"measured_property": "positive shift"
},
{
"quantity": "\u223c\u221227\u2030",
"unit": "\u2030",
"measured_entity": "upper Hartland Shale",
"measured_property": "VPDB \u03b413Corg"
},
{
"quantity": "4.3 m",
"unit": "m",
"measured_entity": "upper Hartland Shale",
"measured_property": "below the CTB"
},
{
"quantity": "\u223c\u221227\u2030",
"unit": "\u2030",
"measured_entity": "\u03b413Corg",
"measured_property": "gradual fall"
}
],
"split": "val",
"docId": "S0012821X13007309-1509",
"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": "The Vocontian Basin was part of the western gulf in the European Alpine region of the NW Tethys Ocean \u223c30\u00b0N (Jarvis et al., 2011; Fig. 1). High rates of subsidence throughout the mid-Cretaceous provided accommodation space for thick rhythmically bedded bioturbated limestone\u2013marl successions, where the variable facies are indicative of a fluctuating hemipelagic depositional environment of moderate depth. Different depositional and structural processes dependent on their location in the basin have affected CTB sections within the Vocontian Basin; e.g. the Vergons section is affected by syn-sedimentary slumping in the uppermost Cenomanian, but otherwise exposes a continuous Upper Albian\u2013Lower Turonian succession, while the thinner Pont d'Issole section is complete through the CTBI. A \u223c20 m thick package of black organic-rich calcareous shales, termed the \u201cNiveau Thomel\u201d (Takashima et al., 2009; Jarvis et al., 2011), characterize the CTBI. Detailed biostratigraphy has been obtained for the 24 m Pont d'Issole section (Grosheny et al., 2006; Jarvis et al., 2011). The distribution of index taxa R. cushmani and H. helvetica, coupled with complete \u03b413Corg and \u03b413Ccarb records (Fig. 2; Jarvis et al., 2011), permits bed-scale correlation with the GSSP near Pueblo. Above the onset of OAE 2, samples were taken from Pont d'Issole, whereas below the onset some of the samples (n=4) came from Vergons (Supplementary Material, Table 2d), which is correlated with Pont d'Issole based on litho-, bio-, and stable-isotope stratigraphy and is undisturbed by faulting in the pre-OAE 2 interval.",
"measurement_extractions": [
{
"quantity": "\u223c30\u00b0N",
"unit": "\u00b0N",
"measured_entity": "Vocontian Basin",
"measured_property": null
},
{
"quantity": "\u223c20 m",
"unit": "m thick",
"measured_entity": "package of black organic-rich calcareous shales, termed the \u201cNiveau Thomel\u201d",
"measured_property": "thick"
},
{
"quantity": "24 m",
"unit": "m",
"measured_entity": "Pont d'Issole section",
"measured_property": null
},
{
"quantity": ")",
"unit": null,
"measured_entity": "some of the samples",
"measured_property": "n"
}
],
"split": "val",
"docId": "S0012821X13007309-1605",
"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 interaction of both volcanism and enhanced global weathering on Osi means that quantifying the magnitude and isolating the extent of the two signals is problematic, since the extent of weathering on seawater chemistry is masked by the inputs from the Caribbean LIP to the global ocean. We can only estimate the Os contribution to seawater chemistry using a mixing model and assumed abundances. If we assume that the average seawater 187Os/188Os prior to the LIP onset was \u223c0.8, and use an average Os abundance in seawater of 10 ppq (based on the present-day average; Peucker-Ehrenbrink and Ravizza, 2000), a basalt 187Os/188Os of 0.13 (Meisel et al., 2001) and an average Os abundance, we can evaluate the approximate Os contribution from the Caribbean LIP to the global ocean using a progressive mixing model (Faure, 1986, Eqs. (9.2) and (9.10)). We note that there are no published Os data for the Caribbean LIP. However, basalts can have variable Os abundances (1 to 600 ppt; Martin, 1991; Crocket and Paul, 2008); typical values range from 1 to 30 ppt (e.g., Shirey and Walker, 1998; Allegre et al., 1999; Dale et al., 2008). Using an Os abundance for a basalt of 30 ppt would require 75% Os contribution from the LIP to yield the least radiogenic Osi observed at all locations. Considerably less Os input from the LIP (25%) is needed if the LIP basalts possess higher Os abundances (100 ppt) and if the Os contribution to seawater also occurred through the addition of gas known to be enriched 20 times that of the basalt (e.g., Yudovskya et al., 2008).",
"measurement_extractions": [
{
"quantity": "\u223c0.8",
"unit": null,
"measured_entity": "seawater",
"measured_property": "187Os/188Os"
},
{
"quantity": "10 ppq",
"unit": "ppq",
"measured_entity": "seawater",
"measured_property": "Os abundance"
},
{
"quantity": "0.13",
"unit": null,
"measured_entity": "basalt",
"measured_property": "187Os/188Os"
},
{
"quantity": "1 to 600 ppt",
"unit": "ppt",
"measured_entity": "basalts",
"measured_property": "Os abundances"
},
{
"quantity": "1 to 30 ppt",
"unit": "ppt",
"measured_entity": "basalts",
"measured_property": "Os abundances"
},
{
"quantity": "30 ppt",
"unit": "ppt",
"measured_entity": "basalt",
"measured_property": "Os abundance"
},
{
"quantity": "75%",
"unit": "%",
"measured_entity": "Os contribution",
"measured_property": "from the LIP"
},
{
"quantity": "25%",
"unit": "%",
"measured_entity": "Os input",
"measured_property": "from the LIP"
},
{
"quantity": "100 ppt",
"unit": "ppt",
"measured_entity": "LIP basalts",
"measured_property": "Os abundances"
},
{
"quantity": "20 times",
"unit": "times",
"measured_entity": "gas",
"measured_property": null
}
],
"split": "val",
"docId": "S0012821X13007309-1989",
"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": "With regards to elemental emissions from limestone use during the calcium looping cycle for CO2 capture, Dean [14] carried out experiments at the bench scale to investigate the effect of coal use on limestone-derived sorbent trace element inventory. Experiments carried out without coal use showed no change in sorbent trace element inventory. Batch experiments carried out using La Jagua coal showed an increase in the concentration of Ba, Cr, K, Mn, Sr, and Ti, whilst concentrations of B, Na and K remain the same, whilst for Cu there was a small decrease. For continuous experiments comparing 2 different coals and refuse-derived fuel (RDF), an increase in Al content was observed suggesting some ash mixed in with the sorbent. The concentrations of Ba, K, Sr and Zn remained the same for all fuels. For Lea Hall coal, there was an increase in B over eight cycles from \u223c20 to 40 ppm, though not for La Jagua. For Cu, sorbent concentrations in the first cycle (\u223c15 ppm) remained the same over eight cycles for the two coals, but increased over five cycles in the presence of RDF from \u223c20 ppm to \u223c180 ppm. Na remained the same for the sorbent cycled in the presence of La Jagua, but there was an increase in results for Lea Hall coal and RDF from \u223c250 to \u223c550 ppm and from \u223c200 to 400 ppm respectively, expected given that the Na content of the La Jagua coal is an order of magnitude lower than of the other two fuels. Ti remained the same for La Jagua over eight cycles, though saw an increase in the sorbent cycled in the presence of RDF over five cycles from \u223c50 ppm to \u223c150 ppm. There appear to be no further studies available in the literature investigating elemental partitioning as a result of limestone use in the calcium looping cycle.",
"measurement_extractions": [
{
"quantity": "from \u223c20 to 40 ppm",
"unit": "ppm",
"measured_entity": "limestone",
"measured_property": "B"
},
{
"quantity": "2",
"unit": null,
"measured_entity": "different coals",
"measured_property": null
},
{
"quantity": "eight",
"unit": null,
"measured_entity": "cycles",
"measured_property": null
},
{
"quantity": "first",
"unit": null,
"measured_entity": "cycle",
"measured_property": null
},
{
"quantity": "\u223c15 ppm",
"unit": "ppm",
"measured_entity": "sorbent",
"measured_property": "Cu"
},
{
"quantity": "eight",
"unit": null,
"measured_entity": "cycles",
"measured_property": null
},
{
"quantity": "two",
"unit": null,
"measured_entity": "coals",
"measured_property": null
},
{
"quantity": "five",
"unit": null,
"measured_entity": "cycles",
"measured_property": null
},
{
"quantity": "from \u223c20 ppm to \u223c180 ppm",
"unit": "ppm",
"measured_entity": "sorbent",
"measured_property": "Cu"
},
{
"quantity": "from \u223c250 to \u223c550 ppm",
"unit": "ppm",
"measured_entity": "sorbent",
"measured_property": "Na"
},
{
"quantity": "from \u223c200 to 400 ppm",
"unit": "ppm",
"measured_entity": "sorbent",
"measured_property": "Na"
},
{
"quantity": "two",
"unit": null,
"measured_entity": "fuels",
"measured_property": null
},
{
"quantity": "eight",
"unit": null,
"measured_entity": "cycles",
"measured_property": null
},
{
"quantity": "five",
"unit": null,
"measured_entity": "cycles",
"measured_property": null
},
{
"quantity": "from \u223c50 ppm to \u223c150 ppm",
"unit": "ppm",
"measured_entity": "sorbent",
"measured_property": "Ti"
}
],
"split": "val",
"docId": "S0016236113008041-3031",
"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 elemental sampling method was undertaken in accordance with US Environmental Protection Agency (EPA) Method 29: Determination of Metals Emissions from Stationary Sources [23]. The experimental set up, as outlined in Fig. 3, comprises a \u2018sampling train\u2019 consisting of several bubblers through which a stack sample of the flue gas is passed. Several bubblers contain aqueous acidic dilution to allow collection of condensed trace elements in the flue gas which passes through. A pump allows the gas to be sampled through the bubblers, and a dry gas meter allows the recording of the volume of gas which is sampled. A glass filter prevents particulate matter from passing through the bubblers. Prior to, and between each experiment, the glassware is acid washed in 10% HNO3 acid in order to prevent contamination. Further details of the procedure are provided in EPA Method 29 [23].",
"measurement_extractions": [
{
"quantity": "10%",
"unit": "%",
"measured_entity": "acid",
"measured_property": "HNO3"
}
],
"split": "val",
"docId": "S0016236113008041-3112",
"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": "Results of ICP-MS flue gas analysis provided in Fig. 5 show that most of the major elements present in the carbonator flue gas increase with the mass of the limestone in the reactor. However, some elements are only present for the largest bed inventory of 13 kg e.g., Ti, Cr, and Mn. However, all elements included within Fig. 5 can be considered to be at very low concentrations of <2 ppm.",
"measurement_extractions": [
{
"quantity": "13 kg",
"unit": "kg",
"measured_entity": "bed inventory",
"measured_property": null
},
{
"quantity": "<2 ppm",
"unit": "ppm",
"measured_entity": "elements",
"measured_property": "concentrations"
}
],
"split": "val",
"docId": "S0016236113008041-3153",
"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. 6 shows the concentrations of minor elements in the solid sorbent, where increasing bed inventory resulted in increasing values observed for Co, Ni, Cu, Mo, Cd, and Sn. In the case of Cu and Sn, values were obtained that were lower than that of the blank for a bed inventory of 4.5 kg, which then increased to values of 22 and 0.22 ppm respectively for 13 kg. In the case of Gd, Dy and U, small decreases in concentration were found for an increasing inventory, however values are low at <1 ppm and the changes observed are small. The remaining elements (Zr, Le, Ce, Pt, and Nd) recorded an increase in concentration for 6 kg over 4.5 kg, and then a decrease from 6 kg to 13 kg. All minor element flue gas concentrations were <0.1 ppm and therefore considered negligible.",
"measurement_extractions": [
{
"quantity": "22",
"unit": "ppm",
"measured_entity": "solid sorbent",
"measured_property": "Cu"
},
{
"quantity": "0.22 ppm",
"unit": "ppm",
"measured_entity": "solid sorbent",
"measured_property": "Sn"
},
{
"quantity": "<1 ppm",
"unit": "ppm",
"measured_entity": "solid sorbent",
"measured_property": "Gd, Dy and U"
},
{
"quantity": "4.5 kg",
"unit": "kg",
"measured_entity": "bed inventory",
"measured_property": null
},
{
"quantity": "13 kg",
"unit": "kg",
"measured_entity": "bed inventory",
"measured_property": null
},
{
"quantity": "6 kg over 4.5 kg",
"unit": "kg",
"measured_entity": "bed inventory",
"measured_property": null
},
{
"quantity": "6 kg to 13 kg",
"unit": "kg",
"measured_entity": "bed inventory",
"measured_property": null
},
{
"quantity": "<0.1 ppm",
"unit": "ppm",
"measured_entity": "minor element",
"measured_property": "flue gas concentrations"
}
],
"split": "val",
"docId": "S0016236113008041-3171",
"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. 7 shows solid concentrations of trace elements, of which only Rb, Nb and W were found at concentrations greater than 0.1 ppm. W showed the greatest change, with concentration increasing from 0.06 to 0.42 to 0.74 ppm for inventories 4.5, 6 and 13 kg respectively. All trace element flue gas concentrations were found at a concentration of <0.1 ppm and therefore considered negligible.",
"measurement_extractions": [
{
"quantity": "greater than 0.1 ppm",
"unit": "ppm",
"measured_entity": "Rb, Nb and W",
"measured_property": "concentrations"
},
{
"quantity": "0.06",
"unit": "ppm",
"measured_entity": "W",
"measured_property": "concentration"
},
{
"quantity": "0.42",
"unit": "ppm",
"measured_entity": "W",
"measured_property": "concentration"
},
{
"quantity": "0.74 ppm",
"unit": "ppm",
"measured_entity": "W",
"measured_property": "concentration"
},
{
"quantity": "4.5, 6 and 13 kg",
"unit": "kg",
"measured_entity": "inventories",
"measured_property": null
},
{
"quantity": "<0.1 ppm",
"unit": "ppm",
"measured_entity": "trace element",
"measured_property": "flue gas concentrations"
}
],
"split": "val",
"docId": "S0016236113008041-3186",
"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": "Elemental impurities have three possible locations for bonding in CaCO3 [26]: Substitution via cation exchange for Ca2+ occurs for 90\u201395% of elements e.g., Fe, Mn, and Sr. Adsorption onto crystal faces to balance charge inbalances e.g., Na, and K. Inclusion of additional mineral phases within the CaCO3.",
"measurement_extractions": [
{
"quantity": "90\u201395%",
"unit": "%",
"measured_entity": "elements",
"measured_property": "Substitution via cation exchange for Ca2+"
}
],
"split": "val",
"docId": "S0016236113008041-3207",
"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. 9 shows that increasing SO2 concentration has some effect on major element concentration in solids. Concentrations of Mg, Al, Si, P, K, Br, Sr, Y, Ba and Pb appear to decrease as SO2 concentration increases. However, there appears to be little difference between concentrations for 1000 and 2000 ppm, the greatest change in concentration being from 0 to 1000 ppm SO2. The trend in the batch experiments whereby concentrations of elements at 1000 ppm were generally lower than at 0 and 2000 ppm, is also seen for some elements in the looping cycle test including Cr, Mn and Fe, but not to the same extent as was seen in the batch tests. Ti shows the same trend for these tests as for single column tests, with 1000 ppm SO2 showing higher concentrations of Ti (135 ppm for single column, 407 ppm for looping test) compared to 0 and 2000 ppm SO2 at values close to 10 ppm Ti in both tests. Zn is the only major element showing a clear increase in concentration with SO2, increasing from 9.4 to 14.1 to 66.8 ppm for SO2 concentrations of 0, 1000 and 2000 ppm respectively.",
"measurement_extractions": [
{
"quantity": "1000 and 2000 ppm",
"unit": "ppm",
"measured_entity": "SO2",
"measured_property": "concentration"
},
{
"quantity": "0 to 1000 ppm",
"unit": "ppm",
"measured_entity": "SO2",
"measured_property": "concentration"
},
{
"quantity": "1000 ppm",
"unit": "ppm",
"measured_entity": "SO2",
"measured_property": "concentration"
},
{
"quantity": "0 and 2000 ppm",
"unit": "ppm",
"measured_entity": "SO2",
"measured_property": "concentration"
},
{
"quantity": "1000 ppm",
"unit": "ppm",
"measured_entity": "SO2",
"measured_property": "concentration"
},
{
"quantity": "0 and 2000 ppm",
"unit": "ppm",
"measured_entity": "SO2",
"measured_property": "concentration"
},
{
"quantity": "135 ppm",
"unit": "ppm",
"measured_entity": "Ti",
"measured_property": "concentrations"
},
{
"quantity": "407 ppm",
"unit": "ppm",
"measured_entity": "Ti",
"measured_property": "concentrations"
},
{
"quantity": "close to 10 ppm",
"unit": "ppm",
"measured_entity": "Ti",
"measured_property": "concentrations"
},
{
"quantity": "9.4",
"unit": "ppm",
"measured_entity": "Zn",
"measured_property": "concentration"
},
{
"quantity": "14.1",
"unit": "ppm",
"measured_entity": "Zn",
"measured_property": "concentration"
},
{
"quantity": "66.8 ppm",
"unit": "ppm",
"measured_entity": "Zn",
"measured_property": "concentration"
},
{
"quantity": "0, 1000 and 2000 ppm",
"unit": "ppm",
"measured_entity": "SO2",
"measured_property": "concentrations"
}
],
"split": "val",
"docId": "S0016236113008041-3269",
"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. 10 shows how gaseous major element concentrations were affected by flue gas SO2 concentration. Concentrations of all were very low at <1 ppm, with Fe and Si present in the highest concentrations at 0.63 and 0.51 ppm respectively. For several elements, 1000 ppm SO2 resulted in either the highest concentrations, as was the case for Na, Al, and Si, or the lowest concentrations, as was the case for Fe, when compared to 0 and 2000 ppm SO2.",
"measurement_extractions": [
{
"quantity": "<1 ppm",
"unit": "ppm",
"measured_entity": "gaseous major element",
"measured_property": "Concentrations"
},
{
"quantity": "0.63",
"unit": "ppm",
"measured_entity": "Fe",
"measured_property": "concentrations"
},
{
"quantity": "0.51 ppm",
"unit": "ppm",
"measured_entity": "Si",
"measured_property": "concentrations"
},
{
"quantity": "1000 ppm",
"unit": "ppm",
"measured_entity": "SO2",
"measured_property": null
},
{
"quantity": "0 and 2000 ppm",
"unit": "ppm",
"measured_entity": "SO2",
"measured_property": null
}
],
"split": "val",
"docId": "S0016236113008041-3290",
"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 minor element 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": "CaCO3"
}
],
"split": "val",
"docId": "S0016236113008041-890",
"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 SO2 on (a) increase of solid major elemental concentrations for SO2 concentrations of 0 ppm, 1000 ppm and 2000 ppm, and bed inventory of 13 kg CaCO3.",
"measurement_extractions": [
{
"quantity": "0 ppm, 1000 ppm and 2000 ppm",
"unit": "ppm",
"measured_entity": null,
"measured_property": "SO2 concentrations"
},
{
"quantity": "13 kg",
"unit": "kg",
"measured_entity": "bed inventory",
"measured_property": "CaCO3"
}
],
"split": "val",
"docId": "S0016236113008041-913",
"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": "Calculated gaseous species (MTDATA) for 4.5 kg, 6 kg and 13 kg sorbent.",
"measurement_extractions": [
{
"quantity": "4.5 kg, 6 kg and 13 kg",
"unit": "kg",
"measured_entity": "sorbent",
"measured_property": null
}
],
"split": "val",
"docId": "S0016236113008041-967",
"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 moon Enceladus, embedded in Saturn\u2019s radiation belts, is the main internal source of neutral and charged particles in the Kronian magnetosphere. A plume of water ice molecules and dust released through geysers on the south polar region provides enough material to feed the E-ring and also the neutral torus of Saturn and the entire magnetosphere. In the time period 2005\u20132010 the Cassini spacecraft flew close by the moon 14 times, sometimes as low as 25 km above the surface and directly through the plume. For the very first time measurements of plasma and energetic particles inside the plume and its immediate vicinity could be obtained. In this work we summarize the results of energetic electron measurements in the energy range 27 keV to 21 MeV taken by the Low Energy Magnetospheric Measurement System (LEMMS), part of the Magnetospheric Imaging Instrument (MIMI) onboard Cassini in the vicinity of the moon in combination with measurements of the magnetometer instrument MAG and the Electron Spectrometer ELS of the plasma instrument CAPS onboard the spacecraft. Features in the data can be interpreted as that the spacecraft was connected to the plume material along field lines well before entering the high density region of the plume. Sharp absorption signatures as the result of losses of energetic electrons bouncing along those field lines, through the emitted gas and dust clouds, clearly depend on flyby geometry as well as on measured pitch angle/look direction of the instrument. We found that the depletion signatures during some of the flybys show \u201cramp-like\u201d features where only a partial depletion has been observed further away from the moon followed by nearly full absorption of electrons closer in. We interpret this as partially/fully connected to the flux tube connecting the moon with Cassini. During at least two of the flybys (with some evidence of one additional encounter) MIMI/LEMMS data are consistent with the presence of dust in energetic electron data when Cassini flew directly through the south polar plume. In addition we found gradients in the magnetic field components which are frequently found to be associated with changes in the MIMI/LEMMS particles intensities. This indicates that complex electron drifts in the vicinity of Enceladus could form forbidden regions for electrons which may appear as intensity drop-outs.",
"measurement_extractions": [
{
"quantity": "2005\u20132010",
"unit": null,
"measured_entity": "time period",
"measured_property": null
},
{
"quantity": "14 times",
"unit": null,
"measured_entity": "the Cassini spacecraft",
"measured_property": "flew close by the moon"
},
{
"quantity": "as low as 25 km",
"unit": null,
"measured_entity": "the Cassini spacecraft",
"measured_property": null
},
{
"quantity": "27 keV to 21 MeV",
"unit": null,
"measured_entity": "electron",
"measured_property": "energy"
}
],
"split": "val",
"docId": "S0019103511004994-1382",
"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 use data taken by the Low Energy Magnetospheric Measurement System (LEMMS), part of the Magnetospheric Imaging Instrument (MIMI) onboard Cassini. MIMI/LEMMS is able to measure the intensities, energy spectra, and pitch angle distributions of energetic charged ions and electrons separately in the energy range between about 20 keV and several tens of MeV. Particles are measured simultaneously from two opposite directions (low-energy telescope (LE) and high-energy telescope (HE)). Low-energy electrons and ions measured in LE are separated by a strong internal magnet. Electrons are bent towards the two different detectors E and F while ions are detected in detectors A and B. The HE consists of a stack of five detectors. The species and energy separation is performed by coincidence measurements between these detectors. Data are recorded in rate channels. The instrument is mounted on top of a movable turntable which rotates about the \u2212y-axis of the spacecraft within 86 s, nominally. The \u2212y-axis points in the direction of the remote sensing instruments (often pointed towards the planet or another object). Under this configuration MIMI/LEMMS is therefore scanning in the x\u2013z-plane of the spacecraft coordinate system allowing very good pitch angle coverage. Unfortunately the turntable stopped rotating in the beginning of 2005 for unknown reasons after the release of the Huygens probe. Therefore during all the Enceladus encounters MIMI/LEMMS was not rotating anymore. Since then the low-energy telescope points at an angle of 77.45\u00b0 away from the \u2212z direction towards the \u2212x-direction (see Fig. 1). The advantage of that non-rotating mode is the time resolution of the instrument which is 16 times better than in the rotating mode (5.65 s) for the rate channels or 0.66 s for the priority channels instead of 86 s for a given pointing/pitch angle). This enables us to measure small-scale, short-lived features in the data set.",
"measurement_extractions": [
{
"quantity": "between about 20 keV and several tens of MeV",
"unit": "MeV",
"measured_entity": "charged ions",
"measured_property": "energy range"
},
{
"quantity": "two",
"unit": null,
"measured_entity": "detectors",
"measured_property": null
},
{
"quantity": "five",
"unit": null,
"measured_entity": "detectors",
"measured_property": null
},
{
"quantity": "86 s, nominally",
"unit": "s",
"measured_entity": "movable turntable",
"measured_property": "rotates about the \u2212y-axis of the spacecraft within"
},
{
"quantity": "77.45\u00b0",
"unit": "\u00b0",
"measured_entity": "low-energy telescope",
"measured_property": "points at an angle"
},
{
"quantity": "16 times",
"unit": "times",
"measured_entity": "a given pointing/pitch angle",
"measured_property": "time resolution of the instrument"
},
{
"quantity": "5.65 s",
"unit": "s",
"measured_entity": "rate channels",
"measured_property": "time resolution of the instrument"
},
{
"quantity": "0.66 s",
"unit": "s",
"measured_entity": "priority channels",
"measured_property": "time resolution of the instrument"
},
{
"quantity": "86 s",
"unit": "s",
"measured_entity": "a given pointing/pitch angle",
"measured_property": "time resolution of the instrument"
}
],
"split": "val",
"docId": "S0019103511004994-1511",
"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 overview of the electron measurements from the MIMI/LEMMS instrument in the vicinity of Enceladus for the flybys E0, E12, and E13 is shown in Fig. 2. Those encounters were slightly north of the equatorial plane. The time period selected is \u00b110 min around closest approach which is marked by a solid line. Absorption signatures are clearly visible in most of the displayed electron channels C1, C3, E0, E6, BE, and G1 (same is true for those not displayed here). The nominal energy passbands are given on the right-hand side of Fig. 2. Only the measurements in channel C1 during flybys E0 and E13 show no absorption signatures (off scale for E13). As mentioned above the region in the vicinity of a moon where electrons are absorbed (upstream or downstream) depends on their energy. Therefore it is important to know the flyby geometry during each encounter. Fig. 3 shows the trajectory of Cassini during the flybys E0, E12, and E13 close to the moon inside of 10 Enceladus radii REnc. We show the spacecraft trajectories projected into the xy-, xz-, and yz-planes of a coordinate system where the moon is in the center (x in plasma flow direction, y towards the planet and z northward). This frame is often referred to as the Enceladus Interaction System (ENIS). The individual flybys are labelled with the flyby numbers E0\u2013E13, respectively. The measured differential intensity of electrons (56\u2013100 keV) as measured in MIMI/LEMMS channel C3 is plotted in a color-code along the trajectory. Blue means low intensities, red1 and white high intensities. Clearly visible is a lack of electrons in this channel in the vicinity of the moon. The highest count rates further away from the moon are more typical of a magnetospheric distribution in that region of Saturn\u2019s magnetosphere. The variation in the measured intensities are a combination of dynamic changes in the magnetosphere and from the fact that MIMI/LEMMS sampled different pitch angles during the individual flybys.",
"measurement_extractions": [
{
"quantity": "\u00b110 min",
"unit": "min",
"measured_entity": "time period",
"measured_property": null
},
{
"quantity": "56\u2013100 keV",
"unit": "keV",
"measured_entity": "electrons",
"measured_property": "measured differential intensity"
}
],
"split": "val",
"docId": "S0019103511004994-1565",
"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": "Rhea is Saturn\u2019s largest icy moon (radius: 1RRh = 764 km). It orbits the planet on an equatorial and circular orbit at a distance of about 8.74Rs from its center (1Rs = 60,268 km). Recent studies showed that Rhea is surrounded by a tenuous exosphere composed of oxygen and carbon dioxide (Teolis et al., 2010). Despite that, the main interaction mode of Rhea with the magnetosphere was shown to be plasma absorption. Magnetic field perturbations in Rhea\u2019s interaction region appear to be guided primarily by the formation of a plasma pressure cavity (wake) downstream of the moon and not from mass or momentum loading from the ionized products of this weak exosphere (Simon et al., 2012; Khurana et al., 2008; Roussos et al., 2008).",
"measurement_extractions": [
{
"quantity": "1RRh",
"unit": "RRh",
"measured_entity": "Rhea",
"measured_property": "radius"
},
{
"quantity": "764 km",
"unit": "km",
"measured_entity": "Rhea",
"measured_property": "radius"
},
{
"quantity": "about 8.74Rs",
"unit": "Rs",
"measured_entity": "circular orbit",
"measured_property": "distance"
},
{
"quantity": "1Rs",
"unit": "Rs",
"measured_entity": null,
"measured_property": null
},
{
"quantity": "60,268 km",
"unit": "km",
"measured_entity": "1Rs",
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512002801-1342",
"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": "Certain channels of LEMMS can accumulate counts with sixteen times higher temporal resolution than the typical rate channel time resolution (\u223c5.7 s). These are called priority channels. The available priority channels for each flyby are given in Table 1. We will distinguish rate and priority channels using the suffix \u201c_PRIO\u201d for the latter (e.g. C1_PRIO). Priority channels are especially useful for the identification of short duration structures, such as the small scale flux dropouts near Rhea (Jones et al., 2008).",
"measurement_extractions": [
{
"quantity": "sixteen times",
"unit": "times",
"measured_entity": "priority channels",
"measured_property": "temporal resolution"
},
{
"quantity": "\u223c5.7 s",
"unit": "s",
"measured_entity": "channels of LEMMS",
"measured_property": "rate channel time resolution"
}
],
"split": "val",
"docId": "S0019103512002801-1496",
"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": "Quantification of this time-of-flight effect gives us an outward radial velocity of about 5 km s\u22121, consistent in direction and order of magnitude with the local radial velocity measured by CAPS (Wilson et al., 2010). Our inferred radial velocity value is a factor of two lower than the one given in Wilson et al. (2010), but this could be due to a series of factors affecting mainly our technique, such as the uncertainties in determining the wake center or the assumption of constant radial velocity for all the time-of-flight interval.",
"measurement_extractions": [
{
"quantity": "about 5 km s\u22121",
"unit": "km s\u22121",
"measured_entity": "outward radial velocity",
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512002801-1608",
"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 relative strength of the observed \u2223B\u2223 disturbances at Rhea with respect to the background \u2223B\u2223 is between 6% and 13%, compared to 6\u20138% at Enceladus, where non-dipolar drifts appear to be important (Krupp et al., 2012). Complex drifts may help energetic electrons access the wake (enhancements), or lead to the formation of forbidden regions (flux dropouts). We therefore believe that the role of complex drifts at Rhea should be investigated.",
"measurement_extractions": [
{
"quantity": "between 6% and 13%",
"unit": "%",
"measured_entity": "Rhea",
"measured_property": "observed \u2223B\u2223 disturbances"
},
{
"quantity": "6\u20138%",
"unit": "%",
"measured_entity": "Enceladus",
"measured_property": "observed \u2223B\u2223 disturbances"
}
],
"split": "val",
"docId": "S0019103512002801-1824",
"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 tracings shown in Fig. 10, 350 electrons were injected at the left boundary. The top right panels show electrons at the 1% level of Erk (1 keV). They practically follow the plasma flow pattern as magnetic drifts are unimportant at low energies. Electron trajectories \u201cgrazing\u201d the wake boundaries expand into the wake due to flow perturbations. The wake then becomes narrower than the moon\u2019s diameter.",
"measurement_extractions": [
{
"quantity": "350",
"unit": null,
"measured_entity": "electrons",
"measured_property": null
},
{
"quantity": "1%",
"unit": "%",
"measured_entity": "electrons",
"measured_property": "level of Erk"
},
{
"quantity": "1 keV",
"unit": "keV",
"measured_entity": "electrons",
"measured_property": "level of Erk"
}
],
"split": "val",
"docId": "S0019103512002801-1849",
"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 equations from Borisov and Mall (2000) and output from the hybrid simulations, we estimate that the flute instability has a linear growth rate of about 0.1\u20130.2 s\u22121 just behind Rhea, giving a growth time of 5\u201310 s, comparable to the time scales inferred particle transport in Rhea\u2019s wake through the observed, narrow channels (Section 5.1). The rate reduces by an order of magnitude 10RRh downstream, and disappears at larger distances, since magnetic field and pressure gradients diminish.",
"measurement_extractions": [
{
"quantity": "0.1\u20130.2 s\u22121",
"unit": "s\u22121",
"measured_entity": "flute instability",
"measured_property": "linear growth rate"
},
{
"quantity": "5\u201310 s",
"unit": "s",
"measured_entity": "flute instability",
"measured_property": "growth time"
},
{
"quantity": "10RRh",
"unit": "RRh",
"measured_entity": null,
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512002801-1927",
"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 simulations originate from two codes developed side-by-side but separately, namely, the Saturn Thermosphere GCM (M\u00fcller-Wodarg et al., 2006) and Saturn 1-D Ionosphere Model (Moore et al., 2004) which were subsequently fully coupled to form the Saturn Thermosphere Ionosphere Model (STIM). The thermosphere component globally solves the non-linear Navier\u2013Stokes equations of momentum, continuity and energy on a spherical pressure level grid. The momentum equation includes terms such as pressure gradients, viscous drag, Coriolis acceleration, curvature accelerations and advection. The energy equation includes all processes of internal energy redistribution, such as advection, adiabatic heating and cooling as well as molecular and turbulent conduction. Solar EUV heating is calculated through explicit line-of-sight integration of solar irradiance attenuation (the Lambert\u2013Beer Law), assuming solar spectra derived from the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED)/Solar EUV Experiment (SEE) (Woods et al., 2005; Woods, 2008) and heating efficiencies of 50%, a value in agreement with estimates for Jupiter by Waite et al. (1983). While we include direct solar EUV heating in our calculations, it has a negligible influence on the energy balance of Saturn\u2019s thermosphere, as shown earlier by M\u00fcller-Wodarg et al. (2006). We show in Section 3 that the main importance of solar EUV radiation lies in its ionising role that leads to conductivities, Joule heating and ion drag which in turn affect the thermospheric energy budget and dynamics.",
"measurement_extractions": [
{
"quantity": "50%",
"unit": "%",
"measured_entity": "Solar EUV heating",
"measured_property": "heating efficiencies"
}
],
"split": "val",
"docId": "S0019103512003533-3299",
"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 STIM GCM calculates the transport by winds and molecular and turbulent diffusion of key neutral species (H, H2, He, CH4, H2O), following the procedures outlined by M\u00fcller-Wodarg et al. (2006). The global spherical grid has flexible resolution. For simulations in this study we assumed spacing in latitude and longitude of 2\u00b0 and 10\u00b0, respectively, and a vertical resolution of 0.4 scale heights. Our time integration step was 5 s and we ran the code for 500 Saturn rotations to reach steady state.",
"measurement_extractions": [
{
"quantity": "2\u00b0",
"unit": "\u00b0",
"measured_entity": "simulations in this study",
"measured_property": "latitude"
},
{
"quantity": "10\u00b0",
"unit": "\u00b0",
"measured_entity": "simulations in this study",
"measured_property": "longitude"
},
{
"quantity": "0.4 scale heights",
"unit": "scale heights",
"measured_entity": "simulations in this study",
"measured_property": "vertical resolution"
},
{
"quantity": "5 s",
"unit": "s",
"measured_entity": "simulations in this study",
"measured_property": "time integration step"
},
{
"quantity": "500",
"unit": null,
"measured_entity": "Saturn rotations",
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512003533-3348",
"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 profiles of noontime ionospheric plasma densities are shown in Fig. 4 for the case of R15. The left panel shows profiles in the region of maximum electron precipitation (78\u00b0) while the right panel shows densities at the sub-solar point (latitude 0\u00b0). Black lines denote the total electron density, blue lines are H+ and red lines H3+ densities. Not shown individually are profiles of other ions calculated in the model, namely H2+, CH3+, CH4+, CH5+, H2O+ and H3O+. The hydrocarbon densities populate the bottomside ionosphere, accounting for most of the electron density below around 1000 km altitude.",
"measurement_extractions": [
{
"quantity": "78\u00b0",
"unit": "\u00b0",
"measured_entity": "region of maximum electron precipitation",
"measured_property": null
},
{
"quantity": "0\u00b0",
"unit": "\u00b0",
"measured_entity": "sub-solar point",
"measured_property": "latitude"
},
{
"quantity": "below around 1000 km",
"unit": "km",
"measured_entity": "altitude",
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512003533-4685",
"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": "Diurnally-averaged thermospheric temperatures, as calculated in simulation R15, are presented in Fig. 6 for the southern hemisphere (with those in the northern hemisphere being identical). We find daily variations of polar temperatures to be less than 6 K and thus virtually negligible, despite the strong diurnal variation of electron precipitation and thereby Joule heating (Fig. 3). The reasons for this are the long thermal time scales in Saturn\u2019s upper atmosphere combined with the fast planetary rotation rate. This justifies discussing diurnally-averaged quantities hereafter.",
"measurement_extractions": [
{
"quantity": "less than 6 K",
"unit": "K",
"measured_entity": "Saturn\u2019s upper atmosphere",
"measured_property": "daily variations of polar temperatures"
}
],
"split": "val",
"docId": "S0019103512003533-4971",
"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 lower latitudes our calculations do not capture observed values well. Fig. 6 shows that exospheric temperatures decrease from around 450 K near the pole to around 180 K near the equator. Voyager 2 UVS occultations of \u03b4-Sco suggested an exospheric temperature of 420 \u00b1 30 K near 29.5\u00b0N (Smith et al., 1983), while a recent reanalysis of Voyager UVS data inferred a value of 488 \u00b1 14 K (Vervack and Moses, 2012). These and other observations suggest low and mid-latitude exospheric temperatures on Saturn to be of the order of 450 K, roughly twice the value shown in Fig. 6. Our model is presently unable to reproduce observed low and mid-latitude exospheric temperatures on Saturn, illustrating that magnetospheric energy is not being transported from the polar to the equatorial regions. This is related to Saturn\u2019s fast rotation rate and the sub-corotation of the auroral thermosphere, which ultimately generates a meridional wind transporting energy from equator to pole in the deep atmosphere, thus cooling down the equatorial regions (Smith et al., 2007). However, since this study is concerned with polar temperatures only we will defer discussion of the equatorial temperature problem to future investigations.",
"measurement_extractions": [
{
"quantity": "around 450 K",
"unit": "K",
"measured_entity": "exospheric temperatures",
"measured_property": null
},
{
"quantity": "around 180 K",
"unit": "K",
"measured_entity": "exospheric temperatures",
"measured_property": null
},
{
"quantity": "420 \u00b1 30 K",
"unit": "K",
"measured_entity": "exospheric temperature",
"measured_property": null
},
{
"quantity": "29.5\u00b0N",
"unit": "\u00b0N",
"measured_entity": null,
"measured_property": null
},
{
"quantity": "488 \u00b1 14 K",
"unit": "K",
"measured_entity": "exospheric temperature",
"measured_property": null
},
{
"quantity": "of the order of 450 K",
"unit": "K",
"measured_entity": "low and mid-latitude exospheric temperatures",
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512003533-5031",
"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 polar forcing via ion drag generates strong westward (sub-corotating) winds at peak velocities of around 1300 m s\u22121 near 82\u00b0 latitude and \u223c1600 m s\u22121 near 78\u00b0 (not shown). In order to relate zonal wind velocities near the ionospheric peak to the degree of corotation of the upper atmosphere, Fig. 8 shows latitudinal profiles of the atmospheric angular velocity relative to Saturn\u2019s rotational velocity, \u03c9/\u03a9S. The solid black line displays the magnetospheric plasma angular velocity of Cowley et al. (2004) from which the electric field used in our simulations (Fig. 2) was derived. The solid blue line is the atmosphere\u2019s diurnally-averaged angular velocity near the ionospheric peak.",
"measurement_extractions": [
{
"quantity": "around 1300 m s\u22121",
"unit": "m s\u22121",
"measured_entity": "strong westward (sub-corotating) winds",
"measured_property": "peak velocities"
},
{
"quantity": "82\u00b0",
"unit": "\u00b0",
"measured_entity": "Saturn",
"measured_property": "latitude"
},
{
"quantity": "\u223c1600 m s\u22121",
"unit": "m s\u22121",
"measured_entity": "strong westward (sub-corotating) winds",
"measured_property": "peak velocities"
},
{
"quantity": "78\u00b0",
"unit": "\u00b0",
"measured_entity": "Saturn",
"measured_property": "latitude"
}
],
"split": "val",
"docId": "S0019103512003533-5072",
"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": "High latitude temperatures in Saturn\u2019s upper atmosphere published until recently had values below \u223c460 K (Melin et al., 2007; Vervack and Moses, 2012), but Melin et al. (2011) and Stallard et al. (2012) have shown that H3+ emission may be brighter than previously indicated, and temperatures hotter. Using high resolution Cassini Visual and Infrared Mapping Spectrometer (VIMS) images, Melin et al. (2011) inferred temperatures of a segment of the auroral oval of 440 \u00b1 50 K. Even higher temperatures in Saturn\u2019s auroral oval of (563\u2013624) \u00b1 30 K were derived from Cassini VIMS observations by Stallard et al. (2012), so auroral temperatures on Saturn up to around 650 K are within the observed range. The thick red line in the upper panel Fig. 12 highlights the 650 K contour line and thus roughly separates values of polar temperatures that have been observed on Saturn (T \u2a7d 650 K) from those that as yet have not been observed (T > 650 K).",
"measurement_extractions": [
{
"quantity": "below \u223c460 K",
"unit": "K",
"measured_entity": "Saturn\u2019s upper atmosphere",
"measured_property": "High latitude temperatures"
},
{
"quantity": "440 \u00b1 50 K",
"unit": "K",
"measured_entity": "auroral oval",
"measured_property": "inferred temperatures"
},
{
"quantity": "(563\u2013624) \u00b1 30 K",
"unit": "K",
"measured_entity": "Saturn\u2019s auroral oval",
"measured_property": "temperatures"
},
{
"quantity": "up to around 650 K",
"unit": "K",
"measured_entity": "Saturn",
"measured_property": "auroral temperatures"
},
{
"quantity": "650 K",
"unit": "K",
"measured_entity": "Saturn",
"measured_property": "polar temperatures"
},
{
"quantity": "\u2a7d 650 K",
"unit": "K",
"measured_entity": "Saturn",
"measured_property": "polar temperatures"
},
{
"quantity": "> 650 K",
"unit": "K",
"measured_entity": "Saturn",
"measured_property": "polar temperatures"
}
],
"split": "val",
"docId": "S0019103512003533-5251",
"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 direct solar EUV heating of Saturn\u2019s upper atmosphere representing a minor energy source only, it is however important to note that solar EUV and shorter wavelength radiation is responsible for the majority of ionisation, and thus conductivity, outside of the narrow band of high latitude electron precipitation. This, in turn, may control thermospheric temperatures. As shown in Fig. 2, the region of magnetospheric electric field is considerably wider than the electron precipitation region, so the Joule heating region extends over a much wider region as well. Therefore, solar radiation does affect high latitude temperatures by means of its role as source of ionisation. This causes hemispheric differences in high latitude temperatures at solstice. In our solstice simulation (R19) we find exospheric temperatures averaged from 74\u00b0S to 90\u00b0S (the summer polar region) of 490 K, while averaging over the same latitude band in the northern (winter) hemisphere gives a value of 430 K. This difference of 60 K is a direct result of enhanced ionisation in the summer hemisphere, leading to stronger Joule heating there. We expect solar cycle variations of high latitude temperatures to lie within the same approximate range, stronger in the summer hemisphere than winter hemisphere, where solar ionisation is considerably weaker. The solar ionisation-induced hemispheric differences in atmospheric conductivity should similarly affect the magnetosphere, highlighting an interesting Sun\u2013atmosphere\u2013magnetosphere coupling chain that deserves more thorough examination in future studies.",
"measurement_extractions": [
{
"quantity": "74\u00b0S to 90\u00b0S",
"unit": "\u00b0S",
"measured_entity": "the summer polar region",
"measured_property": null
},
{
"quantity": "490 K",
"unit": "K",
"measured_entity": "solstice simulation (R19)",
"measured_property": "exospheric temperatures"
},
{
"quantity": "430 K",
"unit": "K",
"measured_entity": "solstice simulation (R19)",
"measured_property": "exospheric temperatures"
},
{
"quantity": "60 K",
"unit": "K",
"measured_entity": "difference",
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512003533-5598",
"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": "Visible and infrared observations have been able to probe the atmosphere of HD209458b at lower altitudes. In fact, HD209458b was the first EGP to have its atmosphere detected by transmission spectroscopy. The first detection was achieved by Charbonneau et al. (2002) who observed a deeper in-transit absorption in the Na D 589.3 nm resonance doublet compared to the adjacent wavelength bands. This detection was based on four transits observed with the Space Telescope Imaging Spectrograph (STIS) onboard the Hubble Space Telescope (HST) (Brown et al., 2001). The same data were later reanalyzed by Sing et al. (2008a,b) who combined them with other observations (Knutson et al., 2007) and created a transmission spectrum of HD209458b at wavelengths of 300\u2013800 nm. They argued that the abundance of sodium in the atmosphere is depleted above the 3 mbar level either by condensation into Na2S clouds or ionization. We note that the detection of Si2+ in the thermosphere (Linsky et al., 2010) constrains cloud formation mechanisms in the upper atmosphere and implies that the depletion of Na at low pressures is probably due to ionization (see Section 3.3).",
"measurement_extractions": [
{
"quantity": "589.3 nm",
"unit": "nm",
"measured_entity": "Na D",
"measured_property": "resonance"
},
{
"quantity": "four",
"unit": null,
"measured_entity": "transits",
"measured_property": null
},
{
"quantity": "300\u2013800 nm",
"unit": "nm",
"measured_entity": "transmission spectrum of HD209458b",
"measured_property": "wavelengths"
},
{
"quantity": "3 mbar",
"unit": "mbar",
"measured_entity": "level",
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512003995-1807",
"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": "Woods et al. (2000) studied the variability of solar Lyman \u03b1 emissions based on satellite observations spanning four and a half solar cycles between 1947 and 1999. They found that the variability ranges between 1% and 37% during one period of solar rotation (27 days), and the average variability during one solar rotation was found to be 9 \u00b1 6%. This result agrees well with the estimated variability of the Lyman \u03b1 emissions from HD209458. The rotation period of HD209458 is estimated to be \u223c10\u201311 days (Silva-Valio, 2008). The G140M observations covered three different transits and took place within a month and a half. Each observation covered approximately 2 h in time. Thus the data can be affected by short-term variability and it is essential that such variability be properly accounted for. For this reason, we compare our models with the results of Ben-Jaffel (2007, 2008) and Ben-Jaffel and Hosseini (2010) who analyzed the data in the time tag mode and accounted for variability before calculating transit depths.",
"measurement_extractions": [
{
"quantity": "four and a half",
"unit": null,
"measured_entity": "solar cycles",
"measured_property": null
},
{
"quantity": "between 1947 and 1999",
"unit": null,
"measured_entity": "satellite observations",
"measured_property": null
},
{
"quantity": "1% and 37%",
"unit": null,
"measured_entity": "Lyman \u03b1 emissions",
"measured_property": "variability"
},
{
"quantity": "9 \u00b1 6%",
"unit": "%",
"measured_entity": "Lyman \u03b1 emissions",
"measured_property": "variability"
},
{
"quantity": "one",
"unit": null,
"measured_entity": "period of solar rotation",
"measured_property": null
},
{
"quantity": "27 days",
"unit": "days",
"measured_entity": "period of solar rotation",
"measured_property": null
},
{
"quantity": "one",
"unit": null,
"measured_entity": "solar rotation",
"measured_property": null
},
{
"quantity": "\u223c10\u201311 days",
"unit": "days",
"measured_entity": "HD209458",
"measured_property": "rotation period"
},
{
"quantity": "three",
"unit": null,
"measured_entity": "transits",
"measured_property": null
},
{
"quantity": "within a month and a half",
"unit": "month",
"measured_entity": "G140M observations",
"measured_property": "took place"
},
{
"quantity": "approximately 2 h",
"unit": "h",
"measured_entity": "observation",
"measured_property": "time"
}
],
"split": "val",
"docId": "S0019103512003995-1910",
"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 interesting to note that while the velocity structure of the escaping plasma can lead to broader absorption that helps to explain the transit depths, it is not necessarily detectable in the data. For instance, Fig. 7 shows the transit depths based on the SOL2 model that has a relatively high radial velocity reaching 11 km s\u22121 by 5Rp. The velocity structure is not detectable because the optical depth of the high velocity material is not sufficient, the LOS velocity at the limb of the planet is slower than the radial velocities in general, and because spectral line broadening within the COS instrument smooths the structure out of the line profiles. If the presence of velocity structure is confirmed in the data (Linsky et al., 2010), it probably implies that there is detached, optically thick plasma moving at large velocities around the planet. If this turns out to be the case, interaction with the stellar wind probably plays a role in giving rise to the observed absorption. Such interaction may also produce turbulence that can broaden the absorption further (e.g., Tian et al., 2005). However, we note that non-thermal broadening such as that proposed by Ben-Jaffel and Hosseini (2010) does not appear to be necessary to explain the current observations.",
"measurement_extractions": [
{
"quantity": "11 km s\u22121",
"unit": "km s\u22121",
"measured_entity": "SOL2 model",
"measured_property": "radial velocity"
},
{
"quantity": "5Rp",
"unit": "Rp",
"measured_entity": null,
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512003995-2760",
"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 agreement with K10, we showed that the H Lyman \u03b1 transit observations (Vidal-Madjar et al., 2003, 2004; Ben-Jaffel, 2007, 2008; Ben-Jaffel and Hosseini, 2010) can be fitted with a layer of H in the thermosphere that is described by three simple parameters. These are the pressure at the bottom of the H layer, the mean temperature in the thermosphere, and a cutoff level due to ionization. The most important parameters are the pressure at the lower boundary and the mean temperature. Because H is the dominant species in the thermosphere, the data can be used to estimate the temperature of the thermosphere. Choosing a lower boundary pressure of 1 \u03bcbar based on the location of the H2/H dissociation front in recent photochemical models (Moses et al., 2011) and observational constraints (France et al., 2010), we estimated a mean temperature of about 8250 K in the thermosphere below 3Rp. However, the uncertainty of the observations is large, and the 1\u03c3 upper and lower limits on this temperature are approximately 6000 K and 11,000 K, respectively.4",
"measurement_extractions": [
{
"quantity": "1 \u03bcbar",
"unit": "\u03bcbar",
"measured_entity": "lower boundar",
"measured_property": "pressure"
},
{
"quantity": "about 8250 K",
"unit": "K",
"measured_entity": "thermosphere below 3Rp",
"measured_property": "temperature"
},
{
"quantity": "approximately 6000 K",
"unit": "K",
"measured_entity": "thermosphere below 3Rp",
"measured_property": "temperature"
},
{
"quantity": "11,000 K",
"unit": "K",
"measured_entity": "thermosphere below 3Rp",
"measured_property": "temperature"
}
],
"split": "val",
"docId": "S0019103512003995-3548",
"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": "Sing et al. (2008a,b) used observations of the Na D line profile to constrain the temperature profile in the upper atmosphere. They suggested that Na condenses into clouds near the 3 mbar level, and thus predicted a deep minimum in temperature in this region that is required for condensation. The detection of Si2+ implies that condensation of Na in the lower atmosphere is unlikely (Paper II), and thus this result is unreliable. Relying on the same observations, Vidal-Madjar et al. (2011a,b) predicted that the temperature increases steeply from 1300 K to 3500 K near the 1 \u03bcbar level. However, their method to retrieve the temperature relies on the density scale height of Na to express the optical depth along the line of sight (LOS). This is not consistent with the argument that Na is depleted above the 3 mbar level. If such a depletion takes place, the density scale height of Na is not the same as the scale height of the atmosphere and it cannot be used to retrieve temperatures.",
"measurement_extractions": [
{
"quantity": "3 mbar",
"unit": "mbar",
"measured_entity": "upper atmosphere",
"measured_property": "level"
},
{
"quantity": "1300 K to 3500 K",
"unit": "K",
"measured_entity": "upper atmosphere",
"measured_property": "temperature"
},
{
"quantity": "1 \u03bcbar",
"unit": "\u03bcbar",
"measured_entity": "upper atmosphere",
"measured_property": "level"
},
{
"quantity": "3 mbar",
"unit": "mbar",
"measured_entity": "upper atmosphere",
"measured_property": "level"
}
],
"split": "val",
"docId": "S0019103512004009-3488",
"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 net heating efficiency of 50% is similar to the heating efficiency in the jovian thermosphere (Waite et al., 1983), and we may consider this as a representative case of a typical gas giant (hereafter, the H50 model). The maximum temperature in the H50 model is 11,500 K and the temperature peak is located near 1.5Rp (p = 0.3 nbar). As \u03b7net varies from 0.1 to 1, the peak shifts from 1.4Rp (0.5 nbar) to 1.9Rp (0.1 nbar) and the maximum temperature varies from 10,000 K to 13,200 K. It is interesting to note that the temperature profile depends strongly on the heating efficiency but the location of the peak and the maximum temperature depend only weakly on \u03b7net. This is because the vertical velocity increases with heating efficiency, leading to more efficient cooling by faster expansion that controls the peak temperature while enhanced advection and high altitude heating flatten the temperature gradient above the peak. As a result, the temperature profile is almost isothermal when \u03b7net = 1.",
"measurement_extractions": [
{
"quantity": "50%",
"unit": "%",
"measured_entity": "typical gas giant (hereafter, the H50 model)",
"measured_property": "net heating efficiency"
},
{
"quantity": "11,500 K",
"unit": "K",
"measured_entity": "H50 model",
"measured_property": "maximum temperature"
},
{
"quantity": "near 1.5Rp",
"unit": "Rp",
"measured_entity": "H50 model",
"measured_property": "temperature peak"
},
{
"quantity": "0.3 nbar",
"unit": "nbar",
"measured_entity": "p",
"measured_property": null
},
{
"quantity": "0.1 to 1",
"unit": null,
"measured_entity": "H50 model",
"measured_property": "\u03b7net"
},
{
"quantity": "1.4Rp (0.5 nbar) to 1.9Rp (0.1 nbar)",
"unit": "Rp",
"measured_entity": "H50 model",
"measured_property": "peak"
},
{
"quantity": "10,000 K to 13,200 K",
"unit": "K",
"measured_entity": "H50 model",
"measured_property": "maximum temperature"
},
{
"quantity": "1",
"unit": null,
"measured_entity": "\u03b7net",
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512004009-3825",
"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": "Koskinen et al. (2010a) inferred a mean temperature of 8250 K in the thermosphere of HD209458b with p0 = 1 \u03bcbar (the M7 model). Taken together with our results based on solar XUV fluxes, this implies a relatively high heating efficiency. Alternatively, with \u03b7net = 0.5 it may imply that the XUV flux of HD209458b is 5\u201310 times higher than the corresponding solar flux. This type of an enhancement is not impossible. The activity level of the star depends on its rotation rate, and the rotation rate of HD209458 may be twice as fast as the rotation rate of the Sun (Silva-Valio, 2008). However, the uncertainty of the observed H Lyman \u03b1 transit depths accommodates a range of temperatures, and thus we are unable to derive firm constraints on the heating rates from the observations. In general, though, the pressure averaged temperature provides a useful connection between observations and temperatures predicted by models that can be exploited to constrain heating rates.",
"measurement_extractions": [
{
"quantity": "8250 K",
"unit": "K",
"measured_entity": "thermosphere of HD209458b",
"measured_property": "mean temperature"
},
{
"quantity": "1 \u03bcbar",
"unit": "\u03bcbar",
"measured_entity": "p0",
"measured_property": null
},
{
"quantity": "0.5",
"unit": null,
"measured_entity": null,
"measured_property": null
},
{
"quantity": "5\u201310 times",
"unit": "times",
"measured_entity": "HD209458b",
"measured_property": "XUV flux"
},
{
"quantity": "twice",
"unit": null,
"measured_entity": "HD209458",
"measured_property": "rotation rate"
}
],
"split": "val",
"docId": "S0019103512004009-3962",
"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 previous section we discussed models where the net heating efficiency \u03b7net was fixed at a constant value at all altitudes. In this section we discuss more realistic models of HD209458b that rely on new approximations of photoelectron heating efficiency and derive an estimate of \u03b7net based on these models. Here we also include radiative cooling from recombination and, in one case, H Lyman \u03b1 emissions by excited H (Murray-Clay et al., 2009). Our aim was to calculate the most likely range of temperatures in the thermosphere of HD209458b based on average solar fluxes. Fig. 4 shows the temperature and velocity profiles at 1\u20135Rp based on different approximations (see Table 2 for the input parameters). Model C1 assumes a constant photoelectron heating efficiency of 93% at all altitudes and photoelectron energies. This heating efficiency is appropriate for photoelectrons created by 50 eV photons at an electron mixing ratio of xe = 0.1 (Cecchi-Pestellini et al., 2009). Model C2 is otherwise similar to C1 but the heating efficiency varies with photoelectron energy and altitude (see below). Models C3 and C4 are also based on C1, but C3 includes the substellar tidal forces in the equations of motion (e.g., Garcia Munoz, 2007) and C4 includes Lyman \u03b1 cooling. All of these models are based on the outflow boundary conditions for temperature, velocity, and density.",
"measurement_extractions": [
{
"quantity": "93%",
"unit": "%",
"measured_entity": "Model C1",
"measured_property": "constant photoelectron heating efficiency"
},
{
"quantity": "50 eV",
"unit": "eV",
"measured_entity": "photons",
"measured_property": null
},
{
"quantity": "0.1",
"unit": null,
"measured_entity": "electron mixing ratio of xe",
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512004009-4007",
"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 an isothermal atmosphere with a temperature T0, Eq. (11) reduces to the famous result for the altitude of the sonic point (Parker, 1958):where is the thermal escape parameter X0 at the lower boundary r0. The isothermal sonic point in the C1 model is located at 7.2Rp where c(\u03bec) = 7.2 km s\u22121. The volume averaged temperature of the C1 model below this point is approximately 7100 K. Assuming that r0 = Rp, T0 = 7100 K, and m = mH, X0 \u223c 16 and Eq. (12) yields \u03bec \u223c 8. In this case the analytic result agrees fairly well with the hydrodynamic model if one accounts for partial ionization of the atmosphere by assuming that the mean atomic weight2 is m = 0.9mH.",
"measurement_extractions": [
{
"quantity": "7.2Rp",
"unit": "Rp",
"measured_entity": "C1 model",
"measured_property": "located at"
},
{
"quantity": "7.2 km s\u22121",
"unit": "km s\u22121",
"measured_entity": "c(\u03bec)",
"measured_property": null
},
{
"quantity": "approximately 7100 K",
"unit": "K",
"measured_entity": "C1 model",
"measured_property": "volume averaged temperature"
},
{
"quantity": "7100 K",
"unit": "K",
"measured_entity": "C1 model",
"measured_property": "T0"
},
{
"quantity": "\u223c 16",
"unit": null,
"measured_entity": "C1 model",
"measured_property": "X0"
},
{
"quantity": "0.9mH",
"unit": "mH",
"measured_entity": "mean atomic weight2",
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512004009-4492",
"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": "Once again, the differences between the earlier models and our work arise from different boundary conditions, and assumptions regarding heating rates and photochemistry. We demonstrate this by reproducing the results of Murray-Clay et al. (2009) with our model. In order to do so, we set the lower boundary to 30 nbar with a temperature of 1000 K, and included the substellar tide in the equations of motion. We only included H, H+, and electrons in the model, and used the recombination rate coefficient and Lyman \u03b1 cooling rate from Murray-Clay et al. (2009). We also calculated the heating and ionization rates with the gray approximation by assuming a single photon energy of 20 eV for the stellar flux of 0.45 W m\u22122 at the orbital position of HD209458b. Fig. 10 shows the density profiles of H and H+ based on this model (hereafter, the MC09 model).",
"measurement_extractions": [
{
"quantity": "30 nbar",
"unit": "nbar",
"measured_entity": "model",
"measured_property": "lower boundary"
},
{
"quantity": "1000 K",
"unit": "K",
"measured_entity": "model",
"measured_property": "temperature"
},
{
"quantity": "20 eV",
"unit": "eV",
"measured_entity": "single photon",
"measured_property": "energy"
},
{
"quantity": "0.45 W m\u22122",
"unit": "W m\u22122",
"measured_entity": "stellar flux",
"measured_property": null
}
],
"split": "val",
"docId": "S0019103512004009-5019",
"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": "Koskinen et al. (2010a) demonstrated that the H Lyman \u03b1 transit observations (Ben-Jaffel, 2007, 2008) can be explained with absorption by H in the thermosphere if the base of the hot layer of H is near 1 \u03bcbar, the mean temperature within the layer is about 8250 K, and the atmosphere is mostly ionized above 3Rp. These parameters are based on fitting the data with a simple empirical model of the upper atmosphere. The density and temperature profiles from our new hydrodynamic model agree qualitatively with these constraints, demonstrating that the basic assumptions of Koskinen et al. (2010a) are reasonable. This confirms once again that a comet-like tail (Vidal-Madjar et al., 2003) or energetic neutral atoms (Holstr\u00f6m et al., 2008) are not necessarily required to explain the H Lyman \u03b1 observations.",
"measurement_extractions": [
{
"quantity": "near 1 \u03bcbar",
"unit": "\u03bcbar",
"measured_entity": "hot layer of H",
"measured_property": "base"
},
{
"quantity": "8250 K",
"unit": "K",
"measured_entity": "hot layer of H",
"measured_property": "mean temperature"
},
{
"quantity": "above 3Rp",
"unit": "Rp",
"measured_entity": "atmosphere",
"measured_property": "mostly ionized"
}
],
"split": "val",
"docId": "S0019103512004009-5507",
"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": "Concentrations of salts in Mars soil assuming deposition during the Amazonian, a soil density of 1 g cm\u22123, and mixing in a range of 1.5\u20132.6 m depth. Values are calculated for the nominal Mars case.",
"measurement_extractions": [
{
"quantity": "1 g cm\u22123",
"unit": "g cm\u22123",
"measured_entity": "Mars soil",
"measured_property": "soil density"
},
{
"quantity": "1.5\u20132.6 m",
"unit": "m",
"measured_entity": "Mars soil",
"measured_property": "depth"
}
],
"split": "val",
"docId": "S0019103513005058-1737",
"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 extremely arid regions on Earth, such as the Atacama Desert, nitrate, sulfate and perchlorate salts form in the atmosphere and accumulate on the surface from dry deposition according to diagnostic evidence in their oxygen isotopes. Salts of similar oxyanions should have formed in the atmosphere of Mars because of comparable photochemical reactions. We use a 1-D photochemical model to calculate the deposition rates of sulfate, nitrogen oxyanions, and perchlorate from Mars\u2019 atmosphere, given a plausible range of volcanic fluxes of sulfur- and chlorine-containing gases in the past. To calculate integrated fluxes over time, we assume that throughout the last 3 byr (the Amazonian eon), the typical background atmosphere would have been similar to today\u2019s cold and dry environment. If the soil has been mixed by impact perturbations to a characteristic depth of \u223c2 m during this time, given a time-average volcanic flux 0.1% of the modern terrestrial volcanic flux, the model suggests that the soil would have accumulated 1.0\u20131.7 wt.% SO42- and 0.2\u20130.4 wt.% N in the form of pernitrate (peroxynitrate) or nitrate. The calculated sulfate concentration is consistent with in situ observations of soils from rovers and landers and orbital gamma ray spectroscopy. However, nitrates or pernitrates are yet to be detected. The modeled formation of perchlorate via purely gas-phase oxidation of volcanically-derived chlorine is insufficient by orders of magnitude to explain 0.4\u20130.6 wt.% ClO4- measured by NASA\u2019s Phoenix Lander. The far smaller amount of ozone in the martian atmosphere compared to the terrestrial atmosphere and the colder, drier conditions are the cause of lower rates of gas phase oxidation of chlorine volatiles to perchloric acid. Our calculations imply that non-gas-phase processes not included in the photochemical model, such as heterogeneous reactions, are likely important for the formation of perchlorate and are yet to be identified.",
"measurement_extractions": [
{
"quantity": "last 3 byr",
"unit": "byr",
"measured_entity": "Amazonian eon",
"measured_property": null
},
{
"quantity": "\u223c2 m",
"unit": "m",
"measured_entity": "soil",
"measured_property": "depth"
},
{
"quantity": "0.1%",
"unit": "%",
"measured_entity": "modern terrestrial volcanic flux",
"measured_property": "time-average volcanic flux"
},
{
"quantity": "1.0\u20131.7 wt.%",
"unit": "wt.%",
"measured_entity": "soil",
"measured_property": "SO42-"
},
{
"quantity": "0.2\u20130.4 wt.%",
"unit": "wt.%",
"measured_entity": "soil",
"measured_property": "N"
},
{
"quantity": "0.4\u20130.6 wt.%",
"unit": "wt.%",
"measured_entity": "soil",
"measured_property": "ClO4-"
}
],
"split": "val",
"docId": "S0019103513005058-3094",
"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 model-inferred abundances of perchlorate are far smaller than observations, however. If we assume the soil is mixed to an average depth of 2 m and the parent salt is Ca(ClO4)2 or Mg(ClO4)2, then we calculate concentrations of 4.3 \u00d7 10\u22128 wt. % and 4.0 \u00d7 10\u221210 wt. %, respectively. These concentrations are both many orders of magnitude lower than the observed abundance at the Phoenix landing site: 0.4\u20130.6 wt. % (Hecht et al., 2009). Also, the concentrations are far below what would be inferred from observations of perchlorate:nitrate ratios \u223c1:60 measured in EETA79001 (Kounaves et al., 2014) and the Atacama perchlorate:nitrate of \u223c1:1000. Distinct from the results for Earth where gas phase reactions were sufficient to reproduce data (Catling et al., 2010), we conclude that additional heterogeneous reactions must be present to account for the efficient formation of perchlorate on Mars, a hypothesis we discuss further in Section 7. But first we explore the sensitivity of deposition fluxes to model parameters.",
"measurement_extractions": [
{
"quantity": "0.4\u20130.6 wt. %",
"unit": "wt. %",
"measured_entity": "perchlorate",
"measured_property": "observed abundance"
},
{
"quantity": "2 m",
"unit": "m",
"measured_entity": "soil",
"measured_property": "depth"
},
{
"quantity": "4.3 \u00d7 10\u22128 wt. %",
"unit": "wt. %",
"measured_entity": "Ca(ClO4)2",
"measured_property": "concentrations"
},
{
"quantity": "4.0 \u00d7 10\u221210 wt. %",
"unit": "wt. %",
"measured_entity": "Mg(ClO4)2",
"measured_property": "concentrations"
},
{
"quantity": "\u223c1:60",
"unit": null,
"measured_entity": "perchlorate:nitrate",
"measured_property": "ratios"
},
{
"quantity": "\u223c1:1000",
"unit": null,
"measured_entity": "perchlorate:nitrate",
"measured_property": "ratios"
}
],
"split": "val",
"docId": "S0019103513005058-4210",
"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 QCM-D instrument, which is a highly sensitive balance based on the measurement of changes in the resonance frequency of a quartz crystal oscillator, was used to study the film formation and complexation processes. The instrument used was a Q-sense E4 microbalance (Q-sense, Gothenburg). The temperature of the measuring cell was controlled at 25 \u00b1 0.02 \u00b0C, and the resonant frequency of the oscillator (f) and the energy dissipation value (D) were recorded simultaneously as a function of time. The baseline was determined using the buffer solution without protein.",
"measurement_extractions": [
{
"quantity": "25 \u00b1 0.02 \u00b0C",
"unit": "\u00b0C",
"measured_entity": "measuring cell",
"measured_property": "temperature"
}
],
"split": "val",
"docId": "S0021979713004438-1415",
"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 adsorption of Mefp-1 on an oxidized iron surface was followed in real time using QCM-D as illustrated in Fig. 2. The sensed mass determined with QCM-D includes the mass of the adsorbed protein and that of water that is hydrodynamically coupled to the layer. This mass will be referred to as the \u201csensed mass\u201d in this article to distinguish it from the adsorbed mass of the protein. The exposure to the Mefp-1 solution leads to initial rapid adsorption, as expected for a protein showing high affinity for the adsorbent. After this initial rapid adsorption, a slower increase in the sensed mass is observed, suggesting a continuous build-up of the Mefp-1 layer. Since the substrate is a pre-conditioned iron surface that is not as stable as a model silica surface, the adsorption of Mefp-1 was interrupted after 10 min to prevent non-negligible corrosion of the substrate within the period of measurement. The protein solution was removed by injection of a protein free 10 \u03bcm FeCl3 solution into the cell, which allows the investigation of Fe3+ enhanced complexation of the pre-adsorbed protein layer. As shown in Fig. 2, the exposure of the adsorbed layer to the FeCl3 solution leads to a decrease in the sensed mass, which can arise from removal of some loosely bound Mefp-1 and/or some of the coupled water in the protein layer. Further information can be obtained from the change in the dissipation (\u0394D) values.",
"measurement_extractions": [
{
"quantity": "10 min",
"unit": "min",
"measured_entity": "adsorption of Mefp-1",
"measured_property": "interrupted"
},
{
"quantity": "10 \u03bcm",
"unit": "\u03bcm",
"measured_entity": "FeCl3",
"measured_property": null
}
],
"split": "val",
"docId": "S0021979713004438-1907",
"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 situ nano-mechanical mapping was performed on an iron substrate with a pre-adsorbed Mefp-1 layer, immersed in the citric acid buffer solution and the 10 \u03bcM FeCl3 solution, respectively. The topography images are shown in Fig. 4, presenting the starting surface in the buffer solution at pH 7\u20138 (Fig. 4a). Since the iron surface was pre-conditioned in 10% NaOH, the surface mainly consists of Fe2O3 as confirmed by the CRM measurement. The Rq value is 3.91 nm over a 500 nm \u00d7 500 nm scanned area. The topograpical images of the Mefp-1 layer adsorbed on the iron surface in the buffer solution and in the FeCl3 solution are shown in Fig. 4b and c, respectively. The adsorption of the protein leads to a smearing of the features seen on the surface prior to the Mefp-1 adsorption, compare Fig. 4a and b. By comparing the Z-range of the surface with and without the Mefp-1 film (Table 1), it is seen that the adsorption of Mefp-1 does not lead to any significant change in the height variation, which indicates a full coverage of the Mefp-1 film on the surface. Fig. 4c presents the topography of the Mefp-1 film exposed to the FeCl3 solution; by comparing with Fig. 4b and the Z-range in Table 1, it is observed that the introduction of Fe3+ induced a negligible decrease in the height variation, which remains predominantly due to the roughness of the substrate surface.",
"measurement_extractions": [
{
"quantity": "10 \u03bcM",
"unit": "\u03bcM",
"measured_entity": "FeCl3",
"measured_property": null
},
{
"quantity": "pH 7\u20138",
"unit": "pH",
"measured_entity": "solution",
"measured_property": null
},
{
"quantity": "10%",
"unit": "%",
"measured_entity": "NaOH",
"measured_property": null
},
{
"quantity": "3.91 nm",
"unit": "nm",
"measured_entity": null,
"measured_property": "Rq value"
},
{
"quantity": "500 nm \u00d7 500 nm",
"unit": "nm",
"measured_entity": "scanned area",
"measured_property": null
}
],
"split": "val",
"docId": "S0021979713004438-1969",
"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 in situ ATR-FTIR spectra of the oxidized iron surface with pre-adsorbed Mefp-1 film obtained after exposure to the 10 \u03bcM FeCl3 solution for different times. Comparing with the 15 min Mefp-1 spectrum (Fig. 6a), the intensity of negative water band is relatively larger after exposing the Mefp-1 layer to the FeCl3 solution (Fig. 8a), which indicates that water is released from the pre-adsorbed Mefp-1 layer. Therefore, it is reasonable to conclude that the addition of the Fe3+ makes the Mefp-1 film more compact, in agreement with the QCM-D and AFM results. It should be noted that the spectra obtained with the addition of FeCl3 are remarkably similar to those without the FeCl3 addition but after sufficiently long exposure in the Mefp-1 solution, which indicates that Fe3+ could also be generated from the oxidized iron surface. We note that the broad and unresolved 1540 cm\u22121 peak is more clearly split into two separate peaks at 1550 cm\u22121 and 1536 cm\u22121 (Fig. 8b). This is not surprising since the DFT analysis showed that the 1540 cm\u22121 peak had contributions from both the backbone and the catechol unit, and that these bands may be influenced differently by the change in aqueous solvation when the water content of the protein film becomes reduced. It should also be recognized that the effect of subtracting the water spectrum may influence the two spectra slightly differently in this region. However, more importantly the peaks, at 1485 cm\u22121 and 1260 cm\u22121 (Fig. 8b), which we have associated with the complexation between catechol and Fe3+, remain largely unaltered both in terms of frequencies and intensities. This supports our earlier hypothesis that Fe3+ could be generated from the oxidized iron surface during the exposure to Mefp-1 solution and forms [Fe(Cat)3]3\u2212 complexes.. The shoulder at 1423 cm\u22121 (marked with arrow in Fig. 8b) seems to be slightly enhanced upon addition of FeCl3, and this could indicate some differences in the geometry of the complexes.",
"measurement_extractions": [
{
"quantity": "10 \u03bcM",
"unit": "\u03bcM",
"measured_entity": "FeCl3",
"measured_property": null
},
{
"quantity": "1540 cm\u22121",
"unit": "cm\u22121",
"measured_entity": "in situ ATR-FTIR spectra",
"measured_property": "peak"
},
{
"quantity": "1550 cm\u22121",
"unit": null,
"measured_entity": "in situ ATR-FTIR spectra",
"measured_property": "peaks"
},
{
"quantity": "1536 cm\u22121",
"unit": "cm\u22121",
"measured_entity": "in situ ATR-FTIR spectra",
"measured_property": "peaks"
},
{
"quantity": "1540 cm\u22121",
"unit": "cm\u22121",
"measured_entity": "in situ ATR-FTIR spectra",
"measured_property": "peak"
},
{
"quantity": "1485 cm\u22121",
"unit": "cm\u22121",
"measured_entity": "in situ ATR-FTIR spectra",
"measured_property": "peaks"
},
{
"quantity": "1260 cm\u22121",
"unit": "cm\u22121",
"measured_entity": "in situ ATR-FTIR spectra",
"measured_property": "peaks"
},
{
"quantity": "1423 cm\u22121",
"unit": "cm\u22121",
"measured_entity": "in situ ATR-FTIR spectra",
"measured_property": "shoulder"
},
{
"quantity": "15 min",
"unit": "min",
"measured_entity": "Mefp-1 spectrum",
"measured_property": null
}
],
"split": "val",
"docId": "S0021979713004438-2148",
"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 figure also demonstrates the capability of FATAL+ to generate pulses with significantly less skew (1 \u03bcs) on top of the FATAL pulses with worst-case skew.",
"measurement_extractions": [
{
"quantity": "1 \u03bcs",
"unit": "\u03bcs",
"measured_entity": "pulses",
"measured_property": "skew"
}
],
"split": "val",
"docId": "S0022000014000026-17824",
"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 the conclusion of our paper, we now assess to which extent the properties of our implementation of the FATAL+ algorithm, which have been expressed and verified within our modeling framework and tested experimentally, meet our design goals. Furthermore, we will discuss a number of potential improvements and future research avenues. Our exposition will follow the optimization criteria listed in Section 2.1.7.\u2022Area consumption: For a suitable implementation, the total number of gates is O(nlogn) per node. This can be seen by observing that the complexity of the threshold gates is dominating the asymptotic number of gates, since the O(n) remaining components of a node have a constant number of gates each; using sorting networks to implement threshold gates, the stated complexity bound follows [48]. Trivially, this number of gates is a factor of O(logn) from optimal. We conjecture that in fact this complexity is asymptotically optimal, unless one is willing to sacrifice other desirable properties of the algorithm (e.g. optimal resilience). Assuming that the gate complexity of the nodes adequately represents the area consumption of our algorithm, we conclude that our solution is satisfactory in that regard.\u2022Communication complexity: Our implementation uses 7 (1-bit) wires per channel, and sequential encoding of the states of the main state machine would reduce this number to 5. All communication are broadcasts. Considering the complexity of the task, there seems to be very limited room for improvement.\u2022Stabilization time: Our algorithm has a stabilization time of O(n) in the worst case. Recent findings [49] show that a polylogarithmic stabilization time can be achieved at a low communication complexity; however, this comes at the expense of suboptimal resilience, a weaker adversarial model, and, most importantly, constants in the complexity bounds that make the resulting algorithm inferior to our solution for any practical range of parameters. Moreover, as formalized in [13] and demonstrated in Section 7, for a wide range of scenarios our algorithm achieves constant stabilization time. Considering the severe fault model, we conclude that despite not being optimal, our algorithm performs satisfactory with respect to this quality measure.\u2022Resilience: It is known that 3f+1 nodes are necessary to tolerate f faults [25,14] unless cryptographic tools are available. Since the complexity incurred by cryptographic tools is prohibitive in our setting, our algorithm features optimal resilience.\u2022Delays: As mentioned, the delay of wires is outside our control. Taking dmin+ and dmax+ into account in the quick cycle machine, we make best use of the available bounds in terms of the final frequency/synchrony trade-off. The delays incurred by the computations performed at nodes are proportional to the depths of the involved circuits. Again, the implementation of the threshold gates is the dominant cost factor here. The sorting network by Ajtai et al. [48] exhibits depth O(logn). Assuming constant fan-in of gates, this is clearly asymptotically optimal if the decision when to increase the logical clock Lv next indeed depends on all n\u22121 input signals of v from remote nodes. We conclude that, so far as within our control, the design goal of minimizing the incurred delays is met by our algorithm.\u2022Metastability: We discussed several effective measures to prevent metastability in Section 6. Our experiments support our theoretical finding that, after stabilization, metastability may not occur in absence of further faults. However, since metastability is an elusive problem for which it is difficult to transfer insights and observations to other modes of operation of a given system\u2014let alone to different implementation technology\u2014a mathematical treatment of metastability is highly desirable. Our model opens up various possible approaches to this issue. For one, it is feasible to switch to a more accurate description of signals in terms of signals' voltages as continuous functions of time. Another option choosing an intermediate level of complexity would be to add an additional signal state (e.g. \u22a5) for \u201cinvalid\u201d signals, representing e.g. creeping or oscillating signals. Assigning appropriate probabilities of metastability propagation and decay to modules, this would enable a unified probabilistic analysis of metastability generation, propagation, and decay within a modeling framework using discrete state representations. Such an approach could yield entirely unconditional guarantees on system recovery; in contrast, our current description requires an a priori guarantee that metastability is sufficiently contained during the stabilization process.\u2022Connectivity: The algorithm presented in this work requires to connect all pairs of nodes and is therefore not scalable. Unfortunately, it is known that \u03a9(n2) links are required for tolerating f\u2208\u03a9(n) faults in the worst case [26,27]. We argued for the assumption of worst-case behavior of faulty nodes; however, it appears reasonable that typical systems will not exhibit a worst-case distribution of faults within the system. Indeed, many interesting scenarios justify to assume a much more benign distribution of faults. In the extreme case where faults are distributed uniformly and independently at random with a constant probability, say, 10%, of a node being faulty, node degrees of \u0394\u2208O(clogn) would suffice to guarantee (at a given point in time) that the probability that more than \u0394/9 neighbors of any node are faulty, is at most 1\u22121/nc. Note that this implies that the mean time until this property is violated polynomially grows with system size. Using the FATAL+ protocol in small subsystems (of less than \u0394 nodes), system-wide synchronization will be much easier to achieve than if one would start from scratch. In this setting, \u0394\u2208O(logn) would replace n in all complexity bounds of the FATAL+ algorithm, resulting in particular in gate complexity O(lognloglogn) per node, computational delay O(loglogn), and stabilization time O(clogn) with probability 1\u22121/nc. Thus, this approach promises \u201clocal\u201d fault-tolerance of \u03a9(\u0394) faults in each neighborhood in combination with excellent scalability in all complexity measures, and realizing this is a major goal of our future work.\u2022Clock size: The constraint (1) entails that either clock size is bounded or large clocks result in larger stabilization time. This restriction can be overcome if we use the clocks of bounded size generated by FATAL+ as input to another layer that runs a synchronous consensus algorithm in order to agree on exponentially larger clocks [41]. Finally, we would like to mention two more prospective extensions of our work. First, building on our modeling framework, it seems feasible to tackle an even more strict verification of the algorithm's properties than \u201cstandard\u201d mathematical analysis. The hierarchical structure and formal specifications of modules seem amenable to formal verification methods. Such an approach should benefit from the possibilities to adjust the granularity of the model by the distinction between basic and compound modules as well as the restrictions imposed by the module specifications; more restrictive modules may be simpler to analyze, yet will guarantee the same properties as the stated variants. Second, it should be noted that it is straightforward to derive clocks of even higher frequency from the FATAL+ clocks. This is essentially done by frequency multiplication, at the expense of increasing the clock skew. We refer to Dolev et al. [13] for details.",
"measurement_extractions": [
{
"quantity": "7",
"unit": null,
"measured_entity": "wires per channel",
"measured_property": null
},
{
"quantity": "1-bit",
"unit": "bit",
"measured_entity": "wires",
"measured_property": null
},
{
"quantity": "10%",
"unit": "%",
"measured_entity": "node",
"measured_property": "probability"
}
],
"split": "val",
"docId": "S0022000014000026-18167",
"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": "Self-reported hearing difficulty was the strongest association with tinnitus. Previous research has shown that around 40% of 55\u201374 year olds with hearing difficulties also report tinnitus [36], and the majority of tinnitus patients have some degree of hearing loss [37]. No single theory explaining the cause of tinnitus is universally accepted, but one theory suggests that tinnitus may result from an increase of central gain (to compensate for deprived sensory inputs) which amplifies neural noise in order to maintain neural homeostasis [38]. It is likely that the severity of tinnitus is influenced by a complex interaction involving auditory, psychological and emotional networks [39,40]. It is interesting that neuroticism is a strong association with current tinnitus, albeit not as strong as hearing difficulty. Furthermore, of those who have tinnitus, neuroticism has a stronger effect on the perceived severity than hearing difficulty. One explanation could be that those who are more neurotic may think their tinnitus is indicative of a more serious condition and therefore find the tinnitus more bothersome. The finding that personality traits such as neuroticism can contribute to tinnitus awareness and distress is important when considering treatment approaches because personality traits are generally stable over time [41], although the absolute level of personality traits can change [42]. Thus it is possible that psychological interventions may be beneficial for tinnitus patients although the effects may be gradual [11].",
"measurement_extractions": [
{
"quantity": "around 40%",
"unit": "%",
"measured_entity": "55\u201374 year olds",
"measured_property": "hearing difficulties"
},
{
"quantity": "55\u201374 year",
"unit": "year",
"measured_entity": null,
"measured_property": null
}
],
"split": "val",
"docId": "S0022399913003358-1044",
"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": "The ternary magnesium nitride fluorides were prepared by the high temperature solid state reaction of stoichiometric amounts of synthesised Mg3N2 with anhydrous MgF2 (Aldrich, 99.9%). All manipulations were carried out under inert nitrogen or argon atmospheres. The appropriate 1:3 and 1:1 stoichiometric molar ratios were used in the synthesis of Mg3NF3 (1) and Mg2NF (2) respectively, as shown below:(1)Mg3N2+3MgF2\u21922Mg3NF3(2)Mg3N2+MgF2\u21922Mg2NF",
"measurement_extractions": [
{
"quantity": "99.9%",
"unit": "%",
"measured_entity": "anhydrous MgF2",
"measured_property": null
},
{
"quantity": "1:3",
"unit": null,
"measured_entity": "stoichiometric molar",
"measured_property": "ratios"
},
{
"quantity": "1:1",
"unit": null,
"measured_entity": "stoichiometric molar",
"measured_property": "ratios"
}
],
"split": "val",
"docId": "S0022459611006116-1160",
"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 products were initially characterised by PXD. Data were collected using a Philips X-pert diffractometer operating with CuK\u03b1 radiation. Initial scans were carried out from 5\u22642\u03b8/\u00b0\u226480 with a step size of 0.02\u00b0 and a scan time of 50 min. Due to the air-sensitive nature of the products, a dedicated air-tight aluminium sample holder with Mylar windows was employed [24].",
"measurement_extractions": [
{
"quantity": "5\u22642\u03b8/\u00b0\u226480",
"unit": "\u00b0",
"measured_entity": "Initial scans",
"measured_property": null
},
{
"quantity": "0.02\u00b0",
"unit": "\u00b0",
"measured_entity": "Initial scans",
"measured_property": "step size"
},
{
"quantity": "50 min",
"unit": "min",
"measured_entity": "Initial scans",
"measured_property": "scan time"
}
],
"split": "val",
"docId": "S0022459611006116-1195",
"dataset": "measeval"
}
] |