dataesr/scientific-paragraphs-categorization huggingface dataset:
The EMPRES-i has tracked AI outbreaks since 2003. When an event could not be linked to an official event from the EMPRES-i, we labelled it as non-official and recorded the epidemiological information provided in the report (i. e. subtype, reported date of the event, the country and location of the event, the host affected, and the number of cases). This enabled us to identify when the same non-official event was reported in different news articles. For both official and non-official events, we calculated the number of non-overlapping events between the two EBS tools, that is, the events that were detected by one tool out of two. For the official events, we evaluated the sensitivity (Se) and timeliness of each tool. Timeliness is the lag in days between the date of official notification to the WOAH (day 0), as recorded in the EMPRES-i database, and the date when the same event was first detected by the PADI-web and HealthMap.
A negative lag means that the EBS tool detects an event in a timely manner, that is, before the date of notification. A positive lag indicated that the EBS tool was untimely for detecting an outbreak, that is, the same day or after the official notification date. Se is defined as the ability of the EBS tool to report an event present in the EMPRES-i database, corresponding to the proportion of true positive events (TP) among the sum of true positive and false-negative (FN) events (Se = TP/(TP+FN)). A TP event was defined as all AI outbreaks in the EMPRES-i database during the study period. An FN event was defined as an event present in the EMPRES-i database that was not detected by an EBS tool. The specificity of event-based surveillance tools cannot be calculated, as it is impossible to assess the status of non-official events detected  [11] ; there may be false positive events as well as TP events not reported to the gold standard databases (WOAH and EMPRES-i).
Our study highlights the complementarity of these two EBS tools. This complementarity reflects the different sources accessed through the EBS pipelines. Our results showed, for instance, that PADI-web captured more local sources than HealthMap, while the latter relied more heavily on social platforms such as Twitter. Barboza et al. [10]  showed that the EBS tool characteristics such as the type of moderation, sources accessed, diseases, languages, and regions covered significantly influence disease detection performance, and that the system's outbreak detection is synergic (complementary). While the proportion of early detected events in our study may seem modest, it is a significant added value to the EBS regarding the reporting of outbreaks of pathogens with zoonotic and pandemic potential.
Alerts could be triggered when WOAH is not mentioned. By providing our corpus and databases with open access, we offer the possibility of evaluating and comparing approaches with a high-quality validation dataset. Both the EBS tools detected several events that could not be found in the EMPRES-i database (S10 Table ). These events may have been local AI events that were not communicated at the international level; thus, they did not appear in the EMPRES-i database. They may also correspond to a suspected event that was negated after a negative laboratory test result for the AI virus or to a false alert, as mentioned in a previous study  [32]. Thus, our study shows that EBS tools can be a source of relevant outbreak information but should be considered complementary to official sources and interpreted with caution.
The IMAGE J software  (Schneider et al. , 2012)  was used for digital analysis. For tissue water extraction, the two youngest fully expanded leaf blades and the leaf growth-and-differentiation zone (LGDZ, see Tissue water was extracted for 2 h using cryogenic vacuum distillation as in  Liu et al. (2016). For sucrose extraction, the youngest fully expanded leaf blades of another two mature tillers from the same plants were excised, placed into paper bags, frozen in liquid nitrogen, stored at -18°C until freeze-drying, milled and stored again at -18°C until sucrose extraction. Water-soluble carbohydrates were extracted from 50 mg aliquots of dry material and sucrose separated from other compounds using a preparative high-performance liquid chromatography (HPLC) technique according to  Gebbing and Schnyder (2001).
This negative RH response of φ LW implied an additional 13% reduction of the RH effect on Δ 18 O LW in the range from 50% to 75% RH. In the present work, we observed a mean φ LW of 0. 37 and an RH-sensitivity of φ LW (-0. 01% -1 ) which was twice that of  Hirl et al. (2019)  and  Zhao et al. (2014). This greater negative RH response of φ LW effectively cancelled the RH effect on Δ 18 O LW in our investigations and was mainly related to the opposite effects of RH on Δ 18 O non-SSW relative to Δ 18 O SSW. Notably, other datasets with a nonsignificant RH response of Δ 18 O LW are also included in the data set of  Cernusak et al. (2022)  and comprise the S. capillata data of  Zhao et al. (2014;  samples collected between 10 AM and 4 PM and reflecting an RH range from 26% to 56%). Notably, the RH sensitivity of Δ 18 O LW which we calculate from the data of  Lehmann et al.
ACKNOWLEDGEMENTS We thank  Anja Schmidt ,  Monika Michler ,  Angela Ernst-Schwärzli ,  Laura Dorn ,  Wolfgang Feneis ,  Richard Wenzel  and  Hans Vogl  for skillful assistance in sampling and sample processing (A. S. , M. M. , A. E. S. , H. V. ), carbohydrate analyses (A. S. , L. D. ), maintenance of the mesocosm facility and gas exchange equipment (W. F. , R. W. ), and manufacturing thermocouples for leaf temperature measurements (R. W. ). We acknowledge preliminary discussions with  Haitao Liu  ( Henan Agricultural University, China ) and  Xiaoying Gong  ( Fujian Normal University, China ), as well as comments of  Rolf Siegwolf  ( Eidg. Forschungsanstalt für Wald, Schnee und Landschaft, WSL, Switzerland ). Comments of  Guillaume Tcherkez  and two anonymous referees helped us greatly to improve the paper.
Means (μ) and standard deviations (σ) are reported. A star symbol indicates a mean RST significantly different from 1 according to a t. test (NS: p > 0. 1,. : p < 0. 1; *: p < 0. 05; **: p < 0. 01, ***: p < 0. 001). RST index was calculated for each pair of each of the four matrices of plant-plant interactions tested in this study: Elite temperate japonica (A: JAPrice, n = 66 pairs/pots), Acuce lines (B: ACUrice, n = 45), Elite Durum wheat (C: ELIwheat, n = 45) and lines from pre-breeding population (D: EPOwheat, n = 45). The data used can be found at https://doi. org/10. 57745/RRA3HL. https://doi. org/10. 1371/journal. pbio. 3002287. g001 susceptibility of at least 1 member of the pair was significantly affected by the identity of its neighbor in 23 mix situations (11% of the cases; In rice, the susceptibility of the focal genotype was significantly higher (up to 67% for MAR cultivated with M20) in 11 mixtures compared to its respective pure stand, whereas it was lower in 2 combinations.
Pathogen inoculation and disease assessment For rice, we selected the multivirulent CL26 strain  [42]  of hemibiotrophic fungal pathogen M. oryzae and performed inoculations as described in  [43]  with a concentration of 100,000 conidia per mL. We inoculated wheat plants with a multivirulent field isolate of P. triticina from southern France  [44]  as described in  [45]. We scanned the symptoms of the latest, well-developed leaf of each focal plant per pot (3 for wheat and 4 for rice) using a resolution of 600 pixels per inch, 6 to 7 days after inoculation. Plants with retarded growth were not scored. We analyzed images with LeAFtool (Lesion Area Finding tool), a home-developed R package available on GitHub depository  [46]  to obtain lesion number and leaf area. Parameter values used for image analysis were at least 10,000 pixels for leaves and 50 pixels for lesion areas, with a blur at 1.
S1 Data. The data has been deposited in a publicly available repository: "Disease levels in binary mixtures of rice and wheat" (https://doi. org/10. 57745/RRA3HL). The susceptibility data measured is given for each matrix. For each matrix are provided the Modalities (summarizes for R the modality), the Experiment (Laboratory internal reference), the focal plant on which symptoms are measured, the corresponding neighbor (plant cultivated with focal), the Species (rice or durum wheat), the Population (JAP rice, ACU rice, EPO wheat, and ELI wheat), the Pathogen used (M. oryzae for rice and P. triticina for wheat), the Lesion number (total lesion number), the Lesion/surface unit (lesion number/unit surface), the corresponding condition (Mix or pure), and, when available, the genetic distance between focal and neighbor.
This work was supported by the  Agence Nationale de la Recherche  ( ANR-16-IDEX-0006 AMUSER  to CV), the  CASDAR program  (" BURRITOS " project to JBM), the  INRAE LIA program  ( Plantomix  project to JBM), the  European Research Council (ERC)  ( Starting Grant Project  " Ecophysiological and biophysical constraints on domestication in crop plants "  ERC-StG-2014-639706-CONSTRAINTS  to CV) and the  National Natural Science Foundation of China  (Grants  31801792  and  31960554  to HH). RP is supported by a PhD grant from  Institut Agro. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Second step involves applying the vegetation model BIOME4 to the high-resolution fields to calculate ecosystem variables (bioclimate and net primary productivity, Table  1 ). Steps 1 and 2 are repeated for each ESM simulations available. These simulations based on different forcings and different models provide a large and diversified calibration dataset. They are a guarantee that the emulator is in some way more robust than the ESM simulations taken separately. Calibration of the emulator (step 3) is done by spatial regression of the ecosystem variables on the forcing variables. Step 4 involves the application of the emulator to the forcings of new time slices or future scenarios. During this step, the annual temperature and precipitation results for the past time slice were compared to paleodata to identify the amplitude of the volcanic and solar activity effects.
1 database for the past periods from  (Klein Goldewijk et al. , 2011)  and for the future periods from  (van Vuuren et al. , 2011). The population varied from less than 10 6 at the LGM to 7 10 9 in 2010 and is projected to be between 9 10 9 and 12. 3 10 9 in 2100 according to the scenario. (iv) Volcano activity (V) represented by the effective aerosol radius deduced from the aerosol optical depth from ice core sulfate records from both polar regions for the last millennium (Crowley and Unterman, 2013) (Fig. 2 ). Its value is 0. 2 when there is no eruption. The maximum value (0. 8) was found in 1258, the year after the Samalas eruption  (Lavigne et al. , 2013). The 21ka, 6ka and future values were set to the pre-industrial values. (v) Solar activity (S) is inferred from 14 C records, the proxy of the total solar irradiance (TSI)  (Muscheler et al.
The future calibration data The future periods are determined by increasingly high GHG concentrations (depending on the scenario, see below), and the other forcing variables were set to the PI value. The simulations were archived by the Coupled Model Intercomparison Project (CMIP). We used simulations of CMIP5  (Taylor et al. , 2012). These include both (i) century-scale integrations, which usually start from a preindustrial control, and climate predictions until the end of the 21 st century, and (ii) near-term integrations for the next 10 to 30 years, which are initialized using observed ocean and sea-ice conditions. The CMIP5 climate change projections are driven by emission scenarios, divided into four classes referred to as "representative concentration pathways" (RCPs)  (Moss et al. , 2010).
5 in approximately 2050 (Fig. 3 ). (iii) The +3°C global warming is reached with a CO2 concentration of 600 ppm and is reached under scenario RCP8. 5 in approximately 2060 (Fig. 3 ). (iv) The +5°C global warming is reached with a CO2 concentration of 900 ppm and is reached under scenario RCP8. 5 in about 2100 (Fig. 3 ); We obtained the corresponding CH4 and N2O concentrations and population sizes from the boundary condition database of CMIP5. The other future forcings are set to the present values. Following  Giorgi and Lionello (2008) , the study area was divided into nine grid boxes (Fig. 4 ). We added a 10th box corresponding to the Gallia Narbonensis province (south of France), the key area for the introduction of viticulture in Gaul. We also considered two other scenarios, based on boundary conditions of +5°C except for volcanic and solar activity.
The temperature and precipitation variables are directly obtained from the simulations downscaled at the 0. 5° grid. The sunshine percentages are obtained by linear regression on temperature and precipitation  (Guiot et al. , 2000). The absolute minimum temperature is derived from the mean temperature of the coldest month according to the quadratic equation of Prentice et al 1992. The soil data are provided by the FAO  (Zobler, 1986). The CO2 atmospheric concentration values are those considered by CMIP5 and PMIP3 as boundary conditions for their simulations. The outputs include net primary production (NPP) of each of the potentially occurring 13 plant functional types (PFT), the total NPP and the corresponding 'biome type', from a set of 28 broad categories (Table  S1 ). The model provides also several bioclimatic variables presented also in Table  1.
The calibration of the emulator (step 3) The numerous model outputs available in the CMIP and PMIP databases (Table  2 ) are used to calibrate robust statistical approximations of coupled ESMs and BIOME4, called emulator  (Kennedy and O'Hagan, 2000). Various emulators are used in climate science  (Tran et al. , 2016; Zhu et al. , 2015; Rougier and Goldstein, 2014; Castruccio et al. , 2014) , including in paleoclimatology  (Strassmann and Joos, 2018; Joos et al. , 1996; Bounceur et al. , 2015). Our approach is the first ESMindependent emulator because it is calibrated using a large set of model simulations under very different scenarios. The calibration is based on a geographically weighted regression  (Brunsdon et al. , 1998) , where the ecosystem variables are expressed as functions of the global forcing variables.
Introduction Continental atmospheric relative humidity (RH) is a key factor of soil evaporation, transpiration, dryness stress, and ecosystem productivity  (Grossiord et al. , 2020; Liu et al. , 2021; López et al. , 2021). However, RH is estimated with low precision in the Earth system models  (IPCC, 2013; Tierney et al. , 2020). Long-term data beyond the instrumental period are needed to improve the representation of RH in these models. Leaf organic and mineral compounds formed dur-Published by Copernicus Publications on behalf of the European Geosciences Union. ing plant growth, such as cellulose, n-alkanes of leaf waxes, or phytoliths, are used as past climate indicators when preserved in soils, sediments, or peat  (Helliker and Ehleringer, 2002a, b; Kahmen et al. , 2011 Kahmen et al. , , 2013;; Zech et al.
, 2018 (Alexandre et al. , , 2019;; Outrequin et al. , 2021). Further, the 17 O-excess phyto is not affected by changes in air temperature or atmospheric CO 2 levels  (Outrequin et al. , 2021). In this study, using a model-data approach, we examined the parameters controlling the triple oxygen isotope composition of bulk grass leaf water and phytoliths at a natural site. For that purpose, a grass plot was equipped to measure for the course of 1 year, all environmental and plant physiological parameters relevant for modeling the isotope composition of the grass leaf water. In particular, the triple oxygen and hydrogen isotope composition of atmospheric water vapor above the grass was measured continuously over the year using a cavity ring-down spectrometer (CRDS), and the grass leaf temperature was monitored at plot scale using an infrared (IR) radiometer.
Each parameter was extracted in hourly resolution from the COOPERATE database  (Reiter et al. , 2015). On sampling days (Table  1 ), stomatal conductance (g s ) and transpiration were monitored continuously over the day on a single grass leaf of 4-5 mm width using a Li-6400 XT gas exchange system (LI-COR Biosciences Inc. , Nebraska, USA). To assess the spatial variability of g s , this parameter was additionally measured hourly on the adaxial side of 10 leaves of at least 3 mm width, randomly selected on the plot, using an AP4 porometer (Table  S1 ; Delta-T Devices LTD, Cambridge, UK). In addition, leaf temperature (T leaf ) was measured in situ on the adaxial side of 10 grass leaves, randomly selected, in 1 h intervals using an Optris CT IR thermometer (Table  S2 ; Optris GmbH, Berlin, Germany).
Irrigation water, precipitation, and atmospheric water vapor A Picarro L2140-i CRDS (California, USA), operated in 17 O dual liquid-vapor mode was installed on-site for the experiment. The isotope composition and mixing ratio of water vapor in the air at 0. 4 m above the grass plot was measured for 70 min every 140 min during the spring monitoring and every 280 min during the monitoring in summer and autumn. In between these measurements, the instrument was used for another experiment. The atmospheric water vapor data from the first 10 min of each measurement cycle were removed to account for memory effects and provide sufficient time to establish a stable baseline. The remaining 60 min was averaged. During the 2 h monitoring, air sampling was performed continuously without interruption. Liquid water standard measurement runs were performed on a weekly basis.
The mean of four measurement runs of liquid water standards was used to normalize the atmospheric water vapor isotope data to VSMOW-SLAP scale. The calibration protocol is described in detail by  Voigt et al. (2022). In brief, three liquid water standards that covered the expected isotope range of atmospheric water vapor at the study site were analyzed at a water mixing ratio of 11 000 ppmv using a Picarro autosampler system (A0325, Picarro Inc. , California, USA) coupled to a high-precision vaporizer (A0211, Picarro Inc. , California, USA). The liquid standards were injected in a dry air stream, produced by a lubricated mobile air compressor (Montecarlo FC2, ABAC air compressors, Italy), further dried using two drierite columns combined with a dry ice trap  (Voigt et al. , 2022). Raw isotope compositions of the liquid standards of four consecutive measurement runs were averaged and then corrected to the water mixing ratio of the measured atmospheric water vapor, using the mean of three mixing ratio dependency functions that were determined on site for water mixing ratios between 3000 and 30 000 ppmv in May 2021, October 2021, and January 2022 (Fig.
A1 ). The precision of calibrated and integrated atmospheric water vapor data was determined using a Monte Carlo simulation  (Voigt et al. , 2022). Precision was better than ± 0. 1 ‰, ± 0. 2 ‰, ± 1. 8 ‰, and ± 14 per meg and ± 0. 9 ‰ for δ 17 O, δ 18 O, δ 2 H, 17 O-excess, and d-excess, respectively. A second Picarro L2140-i CRDS operated in 17 O-highprecision mode was used at CEREGE to analyze the isotope composition of irrigation water and precipitation. Isotope analyses, correction of memory effects, and VSMOW- Sample ID Table  2. Grass and phytolith descriptors, phytolith isotope composition, atmospheric temperature (T air ), plot-scale grass leaf temperature (T plot ), relative humidity (RH), and the ratio between actual atmospheric vapor pressure and saturation vapor pressure at T plot (h) for the three regrowth periods.
The reproducibility for δ 18 O and 17 O-excess measurements of the quartz laboratory standard was 0. 16 ‰ and 8 per meg, respectively (1 SD, n = 5). For the phytolith samples, the precision for δ 18 O and 17 O-excess was always better than 0. 5 ‰ and 12 per meg (1 SD), respectively. The sample measurements were cor-rected using a quartz laboratory standard analyzed at the beginning of the day until a 17 O-excess plateau was reached and again at the end of the day. The isotope composition of the reference gas was determined against NBS28. For robust comparisons between silica and water isotope compositions, the phytolith data are normalized to VSMOW-SLAP scale  (Outrequin et al. , 2021).
and stomatal conductance (g s ) measured on a single leaf blade using the LI-COR gas exchange system, atmospheric temperature (T air ), plot-scale grass leaf temperature (T plot ), relative humidity (RH), water vapor pressure ratio between leaf and the atmosphere (h), and the observed (circles) and predicted steady-state (pale gray curve, Eq. 4) and non-steady-state (orange curve, Eq. 6) isotope composition of F. arundinacea leaf water (δ 18 O, δ 2 H, 17 O-excess, d-excess) from 14-15 June 2021. Error bars of isotope data represent analytical precision (see method section). Shaded areas mark nighttime intervals. The isotope composition of grass leaf water is predicted using average environmental conditions over 30 min before sampling (Tables 1, S5, S6). The pale gray shaded area represents model uncertainty (1 SD) of the predicted steady-state leaf water isotope composition estimated using a Monte Carlo simulation (see caption Fig.
The relationship between 17 O-excess phyto and RH was previously investigated in two growth chamber experiments where F. arundinacea was grown under different conditions of RH (40 %-60 %-80 %) and T air (20-24-28 • C)  (Alexandre et al. , 2018; Outrequin et al. , 2021). The parameters were set constant for more than 10 d, without day-night cycles. Differences in δ 18 O values between source water and atmospheric water vapor were set to 0 ‰ in the first experiment  (Alexandre et al. , 2018)  and to 10 ‰ in the second experiment  (Outrequin et al. , 2021). The two equations obtained from these experiments were statistically similar  (Outrequin et al. , 2021). Linear regression through both datasets (n = 16) gives RH = 0. 22 (± 0. 01) 17 O-excess phyto + 115. 2 (± 3. 9) r 2 = 0. 94. Here, under natural conditions, we investigate whether the RH obtained from Eq.
RH estimated from 17 O-excess phyto can be biased by variations in T leaf-air. This is because the isotope composition of leaf water is not directly determined by RH, but rather the water vapor pressure ratio between the leaf and the atmosphere, i. e. , h (cf. Eq. 2). As discussed in Sect. 4. 2, T leaf-air of -2 • C leads to h values that are 5 %-10 % higher than RH. The calibration line obtained from growth chamber experiments is calibrated for T leaf-air of -2 • C  (Outrequin et al. , 2021). The lower T leaf-air ranging from -1. 1 to 0. 3 • C observed in our study can explain the general underestimation of RH reconstructed from the calibration line (cf. Fig. 7a ). The effect of T leaf-air can be removed when considering h instead of RH. We used the same datasets from the growth chamber experiments as for RH  (Alexandre et al.
Figure3. The 24 h monitoring of F. arundinacea transpiration (E) and stomatal conductance (g s ) measured on a single leaf blade using the LI-COR gas exchange system, atmospheric temperature (T air ), plot-scale grass leaf temperature (T plot ), relative humidity (RH), water vapor pressure ratio between leaf and the atmosphere (h), and the observed (circles) and predicted steady-state (pale gray curve, Eq. 4) and non-steady-state (orange curve, Eq. 6) isotope composition of F. arundinacea leaf water (δ 18 O, δ 2 H, 17 O-excess, d-excess) from 14-15 June 2021. Error bars of isotope data represent analytical precision (see method section). Shaded areas mark nighttime intervals. The isotope composition of grass leaf water is predicted using average environmental conditions over 30 min before sampling (Tables 1, S5, S6).