dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This study would not have been possible without the sequence data provided by the VarGoats Consortium (http: / / www. goatgenome. org/ vargoats. html) and previous work by the International Goat Genome Consortium (IGGC, http: / / www. goatgenome. org/ ) and ADAPTmap Consortium (http: / / www. goatadaptmap. org/ ) providing relevant DNA samples, genotyping tools, and genotyping data through their collaborative networks. Genotypes were funded by several projects: the French Genovicap and Phenofinlait programs (ANR, Apis-Gène, CASDAR, FranceAgriMer, France Génétique Elevage, the French Ministry of Agriculture Agrifood, and Forestry), the European 3SR project, and Maxi'male (CASDAR). We also thank the Capgenes breeding organization for the data provided. We are grateful to the Genotoul bioinformatics platform Toulouse MidiPyrénées and the CTIG (Centre de Traitement de l'Information Génétique) of INRAE Jouy-en-Josas, France, for providing computing resources.
Model design and assumptions Ge partitioning during amorphous silica precipitation was simulated using the multi-component reactive transport software, CrunchTope  (Steefel et al. , 2015). The fractionation model framework within CrunchTope uses a two-endmember solid solution  (Druhan et al. , 2013)  to create a simple, binary mechanical mixture between two substituting elements in a solid phase such as rare and major isotopes of a major element (ex. 28 Si 1-x 30 Si x O 2(am) ) or trace and major elements (ex. Si 1-x Ge x O 2(am) ), where x represents the mole fraction of either the rare isotope or trace element. For the purposes of the current study, an ideal solid solution between amorphous GeO 2 and SiO 2 takes the following form (Eq. (  1 )): ð1 À xÞSi OH ð Þ 4ðaqÞ þ xGeðOHÞ 4ðaqÞ ðSi ð1ÀxÞ Ge x ÞO 2ðamÞ þ 2H 2 O ð1Þ where Ge in the form of Ge(OH) 4(aq) species in solution is shown substituting for Si in amorphous silica, SiO 2(am) , in this solid solution stoichiometry and x represents the mole fraction of GeO 2(am).
A combined d 30 Si and Ge/Si multi-tracer approach has the potential to provide important constraints on weathering intensity, fluid residence times and the amount of secondary silicate formation taking place in the weathering zone. Finally, successful application of the trace element batch model to the experimental dataset over a range of surface areas suggests that the framework developed in the CrunchTope software under the assumption that trace elements behave analogously to stable isotopes is valid for d 30 Si-Ge/Si system. Our results extend the utility of CrunchTope for modeling both kinetic and equilibrium trace element partitioning in addition to stable isotope fractionation.
ACKNOWLEDGMENTS This material is based upon work supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. DGE-1144245 ,  NSF  grant  EAR 1660923 , l' Agence Nationale de la Recherche  project  ANR-17-MPGA-0009 , and by the  Chateaubriand Fellowship of the Office for Science & Technology of the Embassy of France in the United States. Special acknowledgment is given to Jerome Gaillardet,  Julien Bouchez , and the  G2E (Geochimie des Enveloppes Externes) group  for their assistance and allowing us access to the facilities at the  Institut de Physique du Globe de Paris (IPGP) in Paris, France  to perform the experiments and measurements necessary for this study. We extend our thanks to Associate Editor Sophie Opfergelt as well as  Jotautas Baronas  and two anonymous reviewers for their constructive and detailed reviews that have greatly improved this manuscript.
Climate data Historical and climate projections data were used in this study to analyse the likely effect on GHG, productions and soil C stocks in European production systems. We selected two of the four climate scenarios, or "Representative Concentration Pathways" (RCPs) adopted by the IPCC for the fifth Assessment Report (AR5) (IPCC, 2013), an intermediate scenario, RCP4. 5, and a pessimistic one, RCP8. 5. Climate data were provided by the Earth System model HadGEM2-ES  (Collins et al. , 2011)  and were downscaled to a horizontal grid with 0. 5° side resolution in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP;  Warszawski et al. , 2014). Since the spatial resolution of the climatic data is larger than the size selected for the simulation units (0. 25°), 4 adjacent simulation units were subjected to the same meteorological data.
Data were not downscaled to maintain data accuracy as much as possible. Data has been shaped for the European surface (29. 0° to 71. 5° Latitude and -24. 0° to 45. 5° Longitude). HadGEM2-ES model provided daily values of minimum and maximum air temperature, total precipitation, air specific humidity, shortwave radiation and near surface wind speed, for the period 1951-2099. Based on these data, input variables for each model were assigned. The simulation protocol consists of a historical dataset, from 1978 to 2004, constituted in accordance with HadGEM2-ES model using the historical record of climate forcing factors  (Jones et al. , 2011) , and two climate projections RCP4. 5 and RCP8. 5, from 2005 to 2099.
Soil data Soil data were obtained from the European Soil Database (ESDB;  Hiederer, 2013). The ESDB is composed by 1 km × 1 km raster files containing topsoil (0 to 30 cm) and subsoil (30 cm to maximum soil depth) data of clay, silt, sand, gravel, and soil organic carbon (SOC) content, bulk density and maximum root depth. Soil pH for the topsoil was derived at the same spatial detail from the ESDB dataset provided by  Reuter et al. (2008). To define soil characteristics for each spatial simulation unit of 0. 25° side, the most frequent soil was selected, accordingly with their characteristics. Model-specific soil input parameters were calculated on the base of the elementary characteristics (see supplementary material S. 1 for details). For both models, a fixed number of six soil layers was established with a thickness defined as a function of the maximum soil depth.
Crop data Crop species and N fertilisation amount for European Union on the 1 km × 1 km grid were provided in the framework of the GHG-Europe project (EU FP7;  Wattenbach et al. , 2015). These data are based on the statistical crop distribution of Eurostat database (European Statistical Office, 2019a) at regional scale (NUTS2 regions), and the simulation of the CAPRI model (Common Agricultural Policy Regionalized Impact;  Britz and Witzke, 2008 ; see  Leip et al. , 2008). Nitrogen fertilisation amount and the repartition between mineral and organic forms were also provided at NUTS 2 scale. Crop successions were available for the period 1976 to 2010. We only considered the crop successions from the time interval 1978 to 2010 since the crop species used in the two discarded years  (1976) (1977)  were never reused over the time series, and represented less than 1 % of the crop successions (i.
The sowing date was defined as the earliest within the time window, when minimum and maximum temperatures were respectively higher and lower than the thresholds. An additional constraint (no precipitation for three days in a row) was applied to consider farmers practice concerning access to the field. If a suitable sowing date was not identified, a fixed date was imposed in the middle of the time window. Crop database considers a unique crop harvested per year. Residue were managed based on crop type, exporting all the cereals straws (80 % harvested straw of cereals including fodder maize), and leaving the residues of all the other crop types in the field, including grain maize  (Eurostat, 2013). Typical sowing crop densities were imposed based on  Steduto et al. (2012). Fertilisation amounts (kg N ha -1 year -1 ) were defined as the average amounts designed for each of the crops in the most frequent succession in the simulation unit.
Grassland and livestock data Grasslands data considered permanent grassland and rainfed temporary grassland. Nitrogen fertiliser application for European grasslands in a 0. 25° side resolution grid was estimated on the base of regional and national statistics (Eurostat) and CAPRI model  (Leip et al. , 2008). Data were generated combining fertilization managements and nitrogen doses, together with number of mowing events, animal loads, amount of mineral fertilizers and / or organics, and the fraction of leguminous. Mowing dates were defined from temperature using thermal sums (500 °C-days from the first of January) on a base of 5 °C. No cutting was performed before such thermal sums was not obtained. Fertilisation events occurred three days after mowing. Grazing started 30 days after the first mowing event and ended either at the end of the year or at the first freezing period of five consecutive days.
Model validation Simulated crop yields during the historical period ranged between 1. 4 and 44. 8 t ha -1 (as standard humidity) and were in good agreement with EU statistics reported in the Eurostat database (Eurostat, 2020) for the time span 1978-2004 (Fig. 1a ; the time span considered represents the original crop rotation data and complies with the beginning of the climate scenarios). Root Mean Squared Error (RMSE) was equal to 2. 24 t DM ha -1 , Mean Absolute Error (MAE) to 1. 35 t DM ha -1 and the modelling efficiency (Nash and Sutcliff, E) scored 0. 97. Simulations with CERES-EGC overestimated the yields for grain maize, wheat, rye, soybean and sunflower, whereas potato, pulses, rapeseed, barley and sugar beet were slightly underestimated. The relative RMSE (RRMSE) for each crop, individually, ranged from 12.
This underlines the need of targeted agricultural policies at territorial scale aimed to avoid the risk of significant reductions of productivity and mitigate the negative effects of climate change, foremost expected in the second half of the century. Accordingly, this transformational adaptation has to deal with socio-economic and political dynamics, as well as land suitability  (Fischer et al. , 2005; Chaudhary et al. , 2018 , Martin-Lopez et al. , 2019). This work provides a solid database on.
Acknowledgements Funding for this study was provided by the  European Union Seventh Framework Programme   FP7/2007-2013  project  Animal Change FP7-KBBE-2010  Project n°  266018 ,  FACCE ERA-GAS  project  ResidueGAS  and  ADEME  project  AEGES. We would like to thank  B. Gabrielle  and  J-L. Drouet  who provided valuable support to this work.
110 nm, i. e. , twice as much as with Al 3+ at the same pH. Hence, it can be proposed that C7-OH in the iron complexes is dissociated even at low pH (Scheme 2). The colorimetric data of the PSP cyanidin glycosides and their Al 3+ complexes (Table  S2 ) provide additional evidence of the bluing effect induced by raising the pH from 6 to 8, or adding increasing Al 3+ concentrations at a given pH. For comparison, a hue angle of 207. 3 was recorded for the PB-Al 3+ complex at pH 7, i. e. , a close match for that of the synthetic triarylcarbonium colorant Brilliant Blue (h 0 = 209. 4), regarded as a reference for a vibrant cyan hue in the confectionary industry  [8]. No such match was observed with the PSP anthocyanins and the best result recorded, i. e. , the P6′-Al 3+ complex at pH 8 (h 0 = 221.
The colorimetric data of the PSP cyanidin glycosides and their Al 3+ complexes (Table  S2 ) provide additional evidence of the bluing effect induced by raising the pH from 6 to 8, or adding increasing Al 3+ concentrations at a given pH. For comparison, a hue angle of 207. 3 was recorded for the PB-Al 3+ complex at pH 7, i. e. , a close match for that of the synthetic triarylcarbonium colorant Brilliant Blue (h 0 = 209. 4), regarded as a reference for a vibrant cyan hue in the confectionary industry  [8]. No such match was observed with the PSP anthocyanins and the best result recorded, i. e. , the P6 -Al 3+ complex at pH 8 (h 0 = 221. 7) remained off target. However, it confirmed that a single caffeoyl residue at R 1 was sufficient to promote a strong bluing effect.
Competition between Metal Binding and Water Addition Metal binding is accompanied by the removal of the B-ring's phenolic protons. In other words, protons and metal ions compete for the O-atoms of the B-ring. Hence, lowering the pH gradually destabilizes the complexes and a minimal pH for the onset of metal binding is expected. Also, a weaker stability for the complexes could mean reversibility in their formation and thus competition with water addition to the free form (flavylium ion), which leads to color loss. The influence of pH on iron binding was investigated with the two isomers PB and P3. For a given pH, the UV-VIS spectra were recorded immediately after pigment addition to buffer in the absence of Fe 2+ and after maximal binding in the presence of Fe 2+. A rise in visible absorbance in the range 600-700 nm, which is typical of metal binding, could be perceived with both pigments at pH ≥ 3 (data not shown).
Acknowledgments: The authors gratefully thank the Polyphenols analytical platform (UMR  SPO ,  INRAE ,  Montpellier SupAgro ,  Montpellier University, France ) for the HRMS analyses;  Raphaël Plasson  ( UMR408, Avignon University ) for developing the program converting UV-VIS spectra into L*a*b* coordinates; and  Olivia Rossi  ( Avignon University ) for her experimental assistance during her undergraduate internship.
In contrast, across western and central Senegal diversification occurred in response to the droughts of the 1970s and 1980s  (Tacoli, 2011; Hathie and Ba, 2015). In Ilboudo  Nébié et al. (2021; hereafter IN2021) , we analyzed data collected by the UN World Food Programme (WFP) and its in-country partners in three large-scale surveys conducted over the past decade. These surveys sample the nutritional and socioeconomic status of thousands of households at a time, as well as their exposure to shocks. We defined food security based on cluster analysis applied to three variables: -Food Consumption Score (FCS) is a combined measure of food frequency and diet diversity over 7 days. It is computed as the weighted sum of food (group) frequency in household consumption -Food Expenditure Share (FES) is the relative importance of cash expenditure on food compared to total household expenditure.
Materials We analyze data from the same household surveys used in IN2021. The surveys are of the Comprehensive Food Security Vulnerability Analyses (CFSVA) type, and were collected by WFP's Vulnerability and Analysis Mapping (VAM) unit and its government and non-governmental partners  (ENSAN, 2013; ERASAN, 2014; ENSAS, 2016). Data was collected in June 2013, October 2014 and January 2016, allowing the simultaneous sampling of food security across seasons and years. A total of 8,504 households, 5,530 rural, were surveyed in June 2013, which corresponds to the beginning of the rains and sowing, a time when granaries are empty. A total of 5,270 households, all rural, were surveyed in October 2014, which corresponds to the start of harvest in a remarkable drought year. A total of 6,582 households, 4,109 rural, were surveyed in January 2016, which corresponds to the end of the harvest, a time when food is widely available.
Methods As in IN2021, we compute statistics on data from the WFP household surveys in two ways. In the first approach, to assess differences between food security clusters (groups of households sharing similar features), regardless of their geographical location, we stratify household-level values of a given variable based on cluster membership (the group each household belongs to), and then count, average or sum separately across clusters. This is done, for example, in Figures  5, 7. For example, to obtain Figure  5  we calculate the percent of households that derive their consumption of other cereals from each source. Each source of food consumed is coded by a discrete value. To get the allhousehold percentage, we count the occurrences of each value and divide by the total number of respondent households.
DATA AVAILABILITY STATEMENT The household survey data analyzed in this article was collected by the UN World Food Programme in collaboration with government and non-governmental partners in Senegal, including the Executive Secretariat of the National Council on Food Security (SE CNSA), the Directorate of Analysis and Prediction of Agricultural Statistics (DAPSA), the National Agency of Civil Aviation and Meteorology (ANACIM), and World Vision. All personal information which could have enabled the identification of respondent households was removed before the data was made available for analysis. If interested in accessing the data, please make your request to the Vulnerability Analysis and Mapping unit of the Senegal Country Office of the UN World Food Programme. Requests to access these datasets should be directed to WFP Senegal Country Office-WFP.
ACKNOWLEDGMENTS The authors gratefully acknowledge financial support from the  NASA /SERVIR/AST-2 program , through grant  NNX16AN29G , the  French Programme d'Investissements d'Avenir de l'Agence Nationale de la Recherche , through Contract  ANR-17-MPGA-0015  (PRODUCT), the  Earth Institute at Columbia University , the  International Development Research Center , and the  School of Human Evolution and Social Change at Arizona State University.
The setup was designed to control the evolution of the triple oxygen isotope composition of phytoliths and all the water compartments of the soil-plant-atmosphere continuum. Different analytical techniques (cavity ring-down spectroscopy and isotope ratio mass spectrometry) were used to analyze water and silica. An inter-laboratory comparison allowed to strengthen the isotope data matching. Water and phytolith isotope compositions were compared to previous datasets obtained from growth chamber and natural tropical sites. The results show that the δ 18 O value of the source water governs the starting point from which the triple oxygen isotope composition of leaf water, phytolith-forming water and phytoliths evolves. However, since the 17 O excess varies little in the growth chamber and natural source waters, this has no impact on the strong relative humidity dependency of the 17 O excess of phytoliths, demonstrated for the 40 %-80% relative humidity range.
To address these issues, we present the results of a new growth chamber experiment in which the grass species Festuca arundinacea (F. arundinacea) was grown under variable conditions of RH, T atm and CO 2 , mimicking past and present climate conditions in tropical areas. The setup was designed to monitor the evolution of the triple oxygen isotope composition of all the water compartments in the soilplant-atmosphere continuum, particularly that of the atmospheric vapor. Phytolith isotope compositions were analyzed. The isotope data are compared to a previous growth chamber experiment and two datasets obtained from phytolith samples collected along a RH transect in western Africa  (Alexandre et al. , 2018)  and a new isotope monitoring at the soil-plant interface carried out at the African Monsoon Multidisciplinary Analysis -Coupling the Tropical Atmosphere and the Hydrological Cycle (AMMA-CATCH) natural observatory in Benin (western Africa) (this study).
Water vapor and liquid water triple oxygen isotope analyses The humid air of the chambers was analyzed at Ecotron by wavelength-scanned cavity ring-down spectroscopy (CRDS) with a Picarro L2140-i spectrometer operated in 17 O excess mode. For each chamber, the water vapor in the air was measured every second over a 420 min period before switching to the next chamber using a 16-port distribution manifold (Picarro A0311). After discarding the first 20 min to account for potential memory effects, the raw data were averaged over 80 min, resulting in five averages per vapor measurement period. Prior to each 420 min vapor measurement period, three working standards of liquid water were analyzed for calibration. This high calibration frequency allows to counteract a potential drift of the instrument.
The gas was directly sent to the dual-inlet mass spectrometer (ThermoQuest Finnigan Delta V Plus). Each gas sample was run twice. Each run consisted of eight dual-inlet cycles (integration time of 26 s). A third run was performed when the standard deviation (SD) on the first two averages was higher than 15 per meg for 17 O excess. The composition of the reference gas was determined against NBS28. The sample measurements were corrected on a daily basis using a quartz laboratory standard analyzed at the beginning of the day until a 17 O excess plateau was reached and again in the middle and at the end of the day. As recommended by  Sharp and Wostbrock (2021) , isotope compositions obtained from the analyses of NBS28 and six working standards (including phytolith standards) at CEREGE were compared to those obtained from the same standards measured at the University of New Mexico (UNM), where the data are directly calibrated relative to VSMOW-SLAP.
Independent growth chamber and natural datasets used for comparison To examine the effect of the soil water isotope composition on that of phytoliths, the results from the growth chamber experiment were compared with two datasets published in  Alexandre et al. (2018) : (1) the first dataset includes isotope compositions of leaf water and phytoliths of F. arundinacea grown at Ecotron in growth chambers where RH was varied in a similar way to the current RH treatments and T atm was set constant at 25 • C. In the setup of this growth chamber experiment, the isotope composition atmospheric water vapor was not measured. The isotope composition of the irrigation water (-5. 58 ‰ and 26 per meg for δ 18 O and 17 O excess, respectively) was depleted relative to the recent growth chamber experiment but closer to natural waters.
The fogged water had the same isotope composition as the irrigation water, similar to the one used in this study; (2) the second dataset includes isotope compositions of phytoliths extracted from soil tops collected along a vegetation and RH transect in western Africa. The vegetation is represented by savannas and humid forests and soil phytoliths are from grasses and trees. Rainfall δ 18 O weighted monthly means at the sampling locations ranged from -1. 5 ‰ to -4. 5 ‰ (Online Isotopes in Precipitation Calculator -OIPC2-2;  Bowen et al. , 2005). Monthly mean RH extracted from the Climate Research Unit (CRU) 1961-1990 time series ranged from 57 % to 82 %  (Alexandre et al. , 2018). Additionally, a new dataset was obtained from an ongoing monitoring at the AMMA-CATCH natural observatory  (Galle et al.
For the current and 2018 RH treatments, the trends described by the δ 18 O and 17 O excess of leaf water when RH changes are correctly predicted, although some δ 18 O and 17 O excess values are significantly lower than expected from the model. For example, for the current RH treatment, the δ 18 O at 40 % is 6 ‰ lower than predicted. For the 2018 RH treatment, the 17 O excess can be 50 per meg lower than predicted. These model-data discrepancies could be related, as previously suggested  (Alexandre et al. , 2018; Li et al. , 2017) , to the misestimation of the isotope composition of the atmospheric water vapor. The isotope composition of the vapor was measured close to the leaves in the current growth chamber setup. However, it may be heterogeneous at the millimetric scale around the leaves.
97; p value < 0. 0001), (  6 ) where 17 Oexcess LW-SW is the difference of 17 O excess between the LW and the source water (SW). Given the precision ranges, this equation is close to Eq. (  7 ): 17 Oexcess LW-SW (per meg) = 2. 1(±0. 7) RH (%) -248(±44) (r 2 = 0. 55; p value < 0. 05), (  7 ) which is recalculated from the 2018 growth chamber dataset.