dataesr/scientific-paragraphs-categorization huggingface dataset:
ecoinformatics. bromeliads from South et al. (2018) taxa org/#view/doi:10. 5063/ America F1VD6WMF South-east Australian Freshwater Schäfer et al. (2011) 9 traits, described at the Supplementary information freshwater macroinvertebrates from family level for 172 taxa to the article macroinvertebrate traits South-East Australia EPA freshwater biological Freshwater U. S. EPA (2012) Includes functional traits traits database macroinvertebrates from (e. g. , life history, mobility, North America rivers and morphology traits) but streams also ecological and habitat information for 3857 North American taxa. Biological Traits Information Benthic marine macrofauna MARLIN (2006) Includes 40 biological trait Catalogue (BIOTIC) and macroalgae categories. EMODnet Biology database European seaweeds Robuchon et al.
(2015) Functional traits Ongoing work (morphology, life history, ecophysiology) and ecological information (incl. Biogeography) for the 1800 seaweed species listed in Europe. Functional traits of marine European marine Jänes et al. (2017) Functional traits https://www. datadryad. org/ macrophytes macrophytes, including (morphology, resource/doi:10. 5061/ seaweeds ecophysiology) and dryad. 964pf/1 ecological information for 68 species. Polytraits Marine polychaetes Faulwetter 47 traits describing http://polytraits. et al. (2014) morphological, behavioral, lifewatchgreece. eu/ physiological, life-history characteristics, as well as the environmental preferences, for a total of 27,198 trait records for 952 species. The Arctic Traits Database Marine organisms from the Degen and Traits for 478 species-level https://www.
univie. ac. at/ Arctic Faulwetter (2019) taxa. arctictraits/team WoRMS Marine Species Marine species WoRMS Editorial Provides 10 traits that have Traits portal Board (2019) been prioritized within EMODnet Biology, as part of the World Register of Marine Species (WoRMS). https://www. epa. gov/risk/ freshwater-biologicaltraits-database-traits http://www. marlin. ac. uk/ biotic http://www. marinespecies. org/traits/index. php (Continues).
org/10. 1594/ functional traits the Mediterranean Sea 2016) 191 species. PANGAEA. 854331 Freshwater ecology European freshwater Schmidt-Kloiber and Covers environmental https://www. organisms belonging to Hering (2015) preferences, distribution freshwaterecology. info/ fishes, macro- patterns, and functional invertebrates, traits for 20,000 taxa. macrophytes, diatoms and phytoplankton Freshwater benthic diatoms European rivers Rimet and Life-forms, cell-sizes and https://data. inra. fr/dataset. Bouchez (2012) ecological guilds for c. a. xhtml?persistentId=doi:10. 1200 taxa 15454/XLQ40G Fresh and weakly brackish Van Dam et al. (1994) First comprehensive checklist https://doi. org/10. 1007/ waters in the Netherlands of ecological traits (pH, BF02334251 saprobity…) for 948 diatom taxa Phytoplankton of temperate Phytoplankton of temperate Rimet and Database of morphological https://zenodo.
org/record/ lakes lakes Druart (2018) and physiological traits of 1164834#. XRNrPXvgrOR more than 1200 taxa. Freshwater benthic micro- River benthic micro- Neury-Ormanni First integrative database of https://doi. org/10. 1007/ meiofauna meiofauna et al. (2019) 23 morphological traits s10750-019-04120-0 linked to resource acquisition for 35 taxa FishBase Fishes Froese and Provides information on www. fishbase. org Pauly (2019), 34,100 species, including https://doi. org/10. 1594/ Beukhof traits related to trophic PANGAEA. 900866. et al. (2019) ecology and life history. The Coral Trait Database Coral species from the global Madin et al. (2016)) Includes 68,494 coral https://coraltraits. org/ oceans observations with 106,462 trait entries of 158 traits for 1548 coral species. FishTraits Freshwater fishes of the Frimpong and More than 100 traits are United States Angermeier (2010) informed for 809 fish species of the United States, including 731 native and 78 exotic species.
The Kyoto Encyclopedia of A wide range of organisms Kanehisa and Collection of databases on https://www. genome. jp/ Genes and Genomes across the tree of life Goto (2000) genomes and biological kegg/ (KEGG) pathways that provides molecular-level information on gene functions, which could inform on potential functional traits. sFDvent: A global trait Deep-sea hydrothermal-vent Chapman et al. (2019) 646 vent species names, https://doi. org/10. 5061/ database for deep-sea fauna with species-level associated location dryad. cn2rv96 hydrothermal vent fauna identification present or in information, and scores progress for 13 traits.
This method has the technical advantage of reducing the sample size without sacrificing precision. It also has the advantage for paleoclimatic studies that all the measured species can be compared without any age difference. The method is also desired for very old ice cores from the Antarctic interior, with expected resolution for 1. 5-millionyear ice near bedrock of order 10 kyr m -1  (Parrenin et al. , 2017). This paper is structured as follows. Section 2 describes the air extraction from ice and the splitting of the extracted air for the analyses by respective instruments. Section 3 describes the measurements of the sample air with two gas chromatographs and a mass spectrometer. The system performance and precision are evaluated by various tests with standard gases (Sect. 4) and comparisons of our data from ∼ 100 ice-core samples (Dome Fuji, also known as Valkyrie Dome, and NEEM) with published records from other laboratories.
The rest of the standard gas handling and measurements are automated with a custom-made software (with LabVIEW). First, the overall stability of the GC system is assessed by measuring the three standard gases three times at atmospheric pressure. For each standard gas, the peak areas of three consecutive measurements must agree within 1 % to proceed. The linearities of the detector responses are also checked by comparing the middle standard gas concentrations calculated from linear interpolation of the concentration-area relationships of high and low standard gases with the original values. The typical differences are +1. 1 ± 1. 7 ppb for CH 4 , -0. 3 ± 0. 2 ppm for CO 2 and +2. 3 ± 1. 2 ppb for N 2 O. Then, each of the three standard gases is measured at three sub-ambient pressures (i. e. , nine measurements in total) to construct calibration curves for the ice-core measurements.
3 ppm for CO 2 and ∼ 0. 5 ppb for N 2 O as reported in the WMO/IAEA Round Robin Comparison Experiment  (Dlugokencky, 2005; Tsuboi et al. , 2017)  (https: //www. esrl. noaa. gov/gmd/ccgg/wmorr/index. html, last access: 10 December 2020. ). However, at lower concentration levels, inter-calibration between TU and NOAA scales has not been conducted. For CH 4 and N 2 O, we discuss the consistency of calibration scales by comparing our ice-core data with those from other laboratories (see Sect. 5. 1).
The pipette volumes are disconnected from the cans, and the aliquots are expanded into the bellows and equilibrated for 10 min. Then, the bellows are isolated from the inlet and compressed to reach ∼ 34 mbar. The initial pressure in the fully expanded bellows is ∼ 28 mbar from a freshly filled can. The reference can is replaced by a new one when the initial pressure decreases to ∼ 18 mbar. The reference gas from the sample port is measured against the standard side (can vs. can) for four blocks to check the SDs. Before each block, the acceleration voltage is optimized by centering the mass 40 peak, the background is measured after a 120 s idle time and the pressures are adjusted to 5000 ± 50 mV for mass 28 (3 × 10 8 ), automatically with the ISODAT software. The idle time and integration time are 10 and 16 s, respectively.
Long-term stability of standard gas and atmosphere, and normalization of sample ratios For normalization of the ice-core data and monitoring their long-term stability, standard gas in a cylinder (STD-A) and atmosphere (sampled outside the NIPR building) have been regularly measured against the reference can. The atmospheric sampling and measurement procedures follow  Headly (2008)  and  Orsi (2013). Briefly, the atmosphere is collected in a 1. 5 L glass flask with a metal piston pump (Senior Aerospace, MB-158), aspirated air intake and two water traps. The flow rates of the sampling and aspiration lines are 4 and 15 L min -1 , respectively, with a flushing time of > 10 min before sampling. In the laboratory, the flask air is expanded into three volumes in series (∼ 4, ∼ 1. 5 and ∼ 2 mL) and allowed for 30 min to equilibrate, and the air in the middle volume is transferred to a GEPW tube.
We employed the following age scales and synchronizations. For the preindustrial late Holocene (∼ 0 to ∼ 1800 CE), the GICC05 chronology was used for the NEEM core as published by  Rasmussen et al. (2013) , and a WAIS Divide core gas chronology (WDC05A)  (Mitchell et al. , 2013 (Mitchell et al. , , 2011) )  was transferred to the DF core by CH 4 synchronization (the tie points are shown in Fig. 10  and Table 6 ). For the other ice cores for comparisons (including GISP2, WDC, Law Dome cores), we employed their own published timescales (Table  A1 ). The following data were rejected or not acquired due to experimental errors. The DF sample at 144. 75 m lost CH 4 , CO 2 , and N 2 O concentrations and TAC because of a connection failure between the GC and computer. δ 15 N, δ 18 O, δO 2 /N 2 and δAr/N 2 from the DF core at 1521.
06, 1540. 56 and 1712. 10 m were rejected because of the leaky valve on the GEPW tube. The NEEM sample at 217. 15 m showed anomalous CO 2 and N 2 O concentrations as compared with another sample at the same depth (+83 ppm and +47 ppb, respectively); all GC data including CH 4 were rejected for this sample. The NEEM sample at 229. 80 and 438. 83 m showed anomalously low δ 15 N and δ 18 O (half of the typical values, or lower), possibly due to gas handling error or leak. As all the anomalous NEEM data were acquired within 2 months after establishing the method, there would have been experimental errors that slipped our attention.
Anomalously high CH 4 concentrations with similar magnitudes have been reported from GISP2 and NEEM cores measured at other laboratories, and the reason may be CH 4 production after bubble close-off (in the ice sheet) or during air extraction from dusty glacialperiod ice  (Mitchel et al. , 2013; Rhodes et al. , 2013 Rhodes et al. , , 2016;; Lee et al. , 2020). For the purpose of evaluating our system, we exclude the anomalous values from the calculation of pooled SDs and quantitative comparison with other records   (Ahn et al. , 2012; Kawamura, 2001; Kawamura et al. , 2007; Mitchell et al. , 2013; Rubino et al. , 2019; Ryu et al. , 2020; Siegenthaler et al. , 2005). Details are summarized in Table  A1. The DF data are placed on the WDC05A chronology by placing five tie points between the CH 4 variations of the DF and WD cores -thick tick marks at the bottom of panel (a), and all the other data are placed on the respective (published) timescales.
The four discrete values agree within 12 ppb (717. 6, 724. 9, 729. 5 and 722. 9 ppb), excluding again the possibility of major experimental failure for the anomaly. This discrepancy could be due to uncertainty in age synchronization between the cores, a reversal of the NEEM gas age by firn layering (the possibility that the bubbles were closed off in the last stage of firn-ice transition,  Rhodes et al. , 2016)  or CH 4 production within the ice sheet as discussed above. N 2 O concentrations from both polar regions should agree with each other within the uncertainty of ice-core analyses. Our datasets from the NEEM and DF cores agree with each other within ∼ 5 ppb without systematic bias, and they also agree with the Law Dome data by CSIRO within ∼ 5 ppb  (Rubino et al. , 2019). We also compare our data with the Monte Carlo spline fit through the NGRIP, TALDICE, EDML, and EDC data by the University of Bern  (Fischer et al.
, 2019 ; see Fig. A1  for individual data points) and high-resolution data from the NEEM and Styx Glacier ice cores by SNU  (Ryu et al. , 2020). Multi-centennial-scale variations (i. e. , relatively low concentrations around 600 CE and high concentrations around 1100 CE) are commonly seen in all the datasets. However, there appear to be some offsets between the data from NIPR, University of Bern and SNU. The NEEM and Styx Glacier data by SNU are systematically lower by ∼ 5 ppb than our data. Because the NEEM core is measured by both laboratories, the offset cannot be explained by the difference in the original N 2 O concentration in the ice. We examine here the possibility that our method overestimates the N 2 O concentration. Based on the standard gas transfer tests, we do not apply extraction correction for N 2 O concentration.
This raises the possibility that, if our tests indeed underestimate the N 2 O dissolution, then our ice-core data should become lower than the true values. This scenario leads to an upward correction of our dataset and thus does not explain the offset. Therefore, the causes of the systematic offset between the two datasets may be the differences in standard gas scales and calibration methods employed by the two laboratories. The spline curve by the University of Bern shows the depth-dependent offset relative to other datasets. The 2σ error band of the spline curve overlaps well with our data between ∼ 1000 and 1800 CE, but it is systematically lower than our data and agrees with the SNU data for ∼ 0-1000 CE. We measure the ice samples in random order to avoid any apparent trends in the data that might originate in the drifts in the standard gases or instruments (on weekly to monthly timescales).
The sample at 1076 CE (129. 16 m) of the DF core shows very high concentration (∼ 20 ppb higher than the neighboring depths), which is unlikely to be due to experimental errors because the CH 4 and CO 2 concentrations of the same sample are not elevated. Anomalous N 2 O concentrations were also found in late Holocene DF samples in previous measurements  (Kawamura, 2001) , and they are possibly natural artifacts (N 2 O production in ice sheet)  (Kawamura, 2001; Sowers, 2001). The overall agreements of our CH 4 and N 2 O data with the other datasets suggest the reliability of our method and consistency of the TU scales at low concentrations with the NOAA scales. We note that our method does not apply experimental corrections for CH 4 and N 2 O concentrations. For reference, the OSU method (wet extraction with refreezing) applies solubility correction of 1 % (∼ 3-8 ppb) and blank correction of 2.
, 2003). Our new wet-extraction CO 2 values of the DF core for the last 2000 years agree with the Law Dome  (MacFarling Meure et al. , 2006 ), EDML (Siegenthaler et al. , 2005)  and WAIS Divide  (Ahn et al. , 2012)  ice cores mostly within 0 to +10 ppm, with several ∼ 20 ppm deviations. The NEEM wet-extraction CO 2 data are higher than those of DF and other ice cores by ∼ 10-30 ppm (maximum ∼ 60 ppm), which is much larger than the pooled SD (5. 3 ppm for the NEEM dataset). This result is not surprising because it is well known that reliable CO 2 reconstructions are only possible from Antarctic ice cores owing to in situ CO 2 production in the Greenland ice sheet with high impurity concentrations  (Anklin et al. , 1995). Anklin et al. (1995)  measured Eurocore (Greenland) late Holocene ice with both dry-and wet-extraction methods and found that the wet-extraction values were higher (by ∼ 30 to 100 ppm) than the dry-extraction values, which in turn are higher than the Antarctic records by up to ∼ 20 ppm.
pieces were also measured, and the results were compared with those from the inner ice. First, we compare the data from the outer ice and inner ice to validate the magnitude of gas-loss fractionation. For both the bubbly ice and clathrate ice (note that bubble-clathrate transition zone is not investigated), all the measured samples show lower δO 2 /N 2 and δAr/N 2 in the outer ice than those in the inner ice (Fig. 13 ). In addition, δO 2 /N 2 is more depleted than δAr/N 2 , consistent with the gas-loss fractionations found in earlier studies, which proposed that the gasloss fractionation is largely size dependent (molecular diameter of O 2 is smaller than Ar) with weak mass dependency  (Bender et al. , 1995; Huber et al. , 2006a; Severinghaus et al. , 2009). Most samples show higher δ 18 O in the outer ice than in the inner ice.
16c , red marks). Our new δO 2 /N 2 data agree with the gas-loss-corrected old data, suggesting that δO 2 /N 2 as originally trapped in the DF core can be reconstructed from the 20-year-old samples and that the relatively large gas-loss correction by  Kawamura et al. (2007)  was rather accurate. The new δ 15 N and δ 18 O data generally agree with those of  Kawamura (2001)  and  Kawamura et al. (2007)  within the uncertainty of the old data, although the large uncertainties of the old datasets do not permit precise comparisons. The duplicate measurements of bubbly ice of the NEEM core (by removing more than 3 mm from the surface) produced pooled SDs for δ 15 N and δ 18 O (0. 006 ‰ and 0. 009 ‰) similar to those for the DF clathrate ice (Table  7 ). This suggests that the removal of 3 mm is sufficient for the bubbly ice at least for the isotopic ratios, possibly due to generally low pressure of bubbly ice (because it is shallower) compared with clathrate ice.
Thus, we apply constant corrections for δ 15 N and δAr/N 2 , as well as sample-size-dependent correction for δO 2 /N 2. https://doi. org/10. 5194/amt-13-6703-2020 Atmos. Meas. Tech. , 13, 6703-6731, 2020 SDs of duplicate measurements for DF clathrate ice are 3. 2 ppb, 2. 2 ppb, and 2. 9 ppm for CH 4 , N 2 O and CO 2 concentrations, respectively, and 0. 006 ‰, 0. 011 ‰, 0. 09 ‰ and 0. 12 ‰ for δ 15 N, δ 18 O, δO 2 /N 2 and δAr/N 2 , respectively. The CH 4 and N 2 O data from the DF and NEEM ice cores for the last 2000 years agree well with those from the GISP2, WAIS Divide and Law Dome cores. We also demonstrate significant gas-loss-induced depletion of δO 2 /N 2 in the ice near the sample surface of the DF clathrate ice, which has been stored at -50 • C over ∼ 20 years. The original δO 2 /N 2 , δAr/N 2 , δ 15 N and δ 18 O in the ice sheet may still be obtained by removing the sample surface by > 8 mm.
Acknowledgements. We thank  Yumiko Miura  for a part of the data reduction;  Akito Tanaka ,  Satoko Nakanishi ,  Mariko Hayakawa  and  Satomi Oda  for the measurements; and  Shinji Morimoto  for providing the  Tohoku University  scales and assisting our standard gas calibrations. We acknowledge the Dome Fuji drilling projects led by  Okitsugu Watanabe ,  Yoshiyuki Fujii , and  Hideaki Motoyama  and participants in the  Japanese Antarctic Research Expedition and Ice Core Consortium  related to the Dome Fuji fieldwork and management. NEEM is directed and organized by the  Center of Ice and Climate at the Niels Bohr Institute  and  US NSF, Office of Polar Programs. It is supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO),  Canada (NRCan/GSC), China (CAS) ,  Denmark (FIST), France (IPEV, CNRS/INSU, CEA and ANR) ,  Germany (AWI) ,  Iceland (RannIs), Japan (NIPR) ,  Korea (KOPRI) , the  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland (SNF) , the  United Kingdom (NERC) , and the  USA (US NSF, Office of Polar Programs).
We are grateful for constructive comments, remarks and advice from  Jochen Schmitt  and an anonymous reviewer. Financial support. This research has been supported by the  JSPS KAKENHI  (grant nos. 17K12816 ,  17J00769 ,  20H04327 ,  20H00639 ,  15KK0027 ,  26241011 ,  21671001 ,  18749002 ,  22221002 ,  18K01129  and  22310003 ); the  Ministry of Education, Culture, Sports, Science and Technology of Japan  (grant no. 17H06316  and  GRENE Arctic Climate Change Research Project, Arctic Challenge for Sustainability II (ArCS II ), program grant number  JPMXD1420318865 ); the  Ministry of the Environment of Japan  (Global Environment Research Coordination System); and the  National Institute of Polar Research  (grant no. KP305  and  Senshin  Project). Review statement. This paper was edited by  Frank Keppler  and reviewed by  Jochen Schmitt  and one anonymous referee.
Data availability. Gas data of the Dome Fuji and NEEM ice cores are available at the NIPR ADS data repository (https://doi. org/10. 17592/001. 2020050101; Oyabu et al. , 2020)  and will be available on the NOAA paleoclimate database. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-13-6703-2020-supplement.
For their glucose supply, ruminants are highly dependent on the endogenous synthesis in the liver, but despite the numerous studies that evaluated hepatic glucose production, very few simultaneously measured hepatic glucose production and uptake of all precursors. As a result, the variability of precursor conversion into glucose in the liver is not known. The present study aimed at investigating by meta-analysis the relationships between hepatic glucose net release and uptake of precursors. We used the FLuxes of nutrients across Organs and tissues in Ruminant Animals database, which gathers international results on net nutrient fluxes at splanchnic level measured in catheterized animals. Response equations were developed for intakes up to 41 g DM intake/kg BW per day of diets varying from 0 to 100 g of concentrate/100 g DM in the absence of additives.
Material and methods Selection of relevant publications from the FLuxes of nutrients across Organs and tissues in Ruminant Animals database We used the Flora (FLuxes of nutrients across Organs and tissues in Ruminant Animals;  Vernet and Ortigues-Marty, 2006)  database built from approximately 250 international publications on splanchnic fluxes in multi-catheterized ruminants. Publications anterior to 2008 were used for model development. To be eligible, publications had to report the net hepatic fluxes (see Supplementary Material S1 for definitions) of glucose or one of its major precursors, propionate, total amino acids (measured in the selected publication by its proxy α-amino-N) and L-lactate  (Hammon et al. , 2016). Publications or treatments reporting intra-venous infusion of nutrients, use of digestion or metabolism modifiers or non-steady state feeding were discarded.
Five eligible datasets were identified to study propionate, α-amino-N, L-lactate, glucose and infusion of propionate with respectively 29 (69), 26 (65), 25 (66), 46 (106) and 5 (16) experimental groups (treatments). No publication was available that reported total amino acids fluxes, only α-amino-N fluxes were available. Insulin concentrations and fluxes were also considered in a separate dataset when available (9 experimental groups, 19 treatments). The list of references used for the meta-analysis is given in Supplementary Material S2. Because the selected publications did not systematically report the chemical composition and nutritional value of feeds and diets, the feeds and diets from all relevant publications were described according to the  INRA Feed Evaluation System (2007)  as detailed by  Loncke et al.
Meta-analyses The meta-analyses focused on the responses of the different net hepatic fluxes (Y variables) to variations in explanatory variables (X) intra-studies, as detailed in  Sauvant et al. (2008)  and  Loncke et al. (2015). Net hepatic fluxes are defined as the difference between hepatic efferent and afferent fluxes of nutrients measured in blood or plasma, as detailed in  Loncke et al. (2015). Net fluxes can be either positive indicating a net hepatic release or negative indicating a net uptake. Net portal appearance is calculated from the difference between portal venous and arterial fluxes. Description of the meta-design. The meta-design was described by statistics (mean, SD, range of values) generated for each parameter in the selected datasets. Species and physiological status effects were tested by one-way ANOVA.