dataesr/scientific-paragraphs-categorization huggingface dataset:
The authors proposed further studies to investigate the possibility of using non-irrigated agricultural pixels instead of natural areas, which is in line with the approach presented in this study. In general, the methodology follows a similar development as the   2020 ), since they are both adapted from a precipitation correction approach employing SM observations into a simple model. One particular difference between these two methodologies is the use of a different modeling approach and data assimilation: while SM2RAIN uses the original water balance equation and directly assimilates SM observations into the model, PrISM uses the API formula and assimilates SM observations through a particle filter approach. The API formula proposed might be considered a solution to the water balance ordinary differential equation, as shown in  Pan et al.
When applied at the field scale, it is also beneficial to provide the methodology with a map of irrigation systems, which is used to constrain the irrigation frequency (daily for modern irrigation systems such as sprinkler and drip, and biweekly for traditional flood irrigation systems). Maps of irrigation systems have been recently developed  (Paolini et al. , 2022)  and could be applied to this methodology in future iterations over larger areas. Future work should be directed towards studying the influence of different precipitation datasets on irrigation retrieval and assessing the uncertainties created by the use of remotely sensed meteorological data, which is expected to be considerable  (Foster et al. , 2020). For this study,   5 , but for a synthetic study where the pixel is only partially irrigated and different percentages of irrigation are presented.
In particular, when using a precipitation dataset with a lower temporal resolution in PrISM, it is expected that the maximum and minimum irrigation scenarios will tend to be closer to each other and the quality of the amounts of irrigation will decrease, up to the edge case where precipitation and soil moisture used have the same temporal resolution, and the two scenarios represent the same amount of irrigation. As demonstrated by  Jalilvand et al. (2019) , implementing a bias correction post-processing step in the methodology improves the retrieval of irrigation amounts. This approach removes the bias by subtracting the false irrigation amounts detected in the adjacent non-irrigated pixel from the actual irrigated pixel. This post-processing step could be a straightforward implementation in PrISM, as adjacent non-irrigated pixels are already identified and used for calibration.
With this approach, false detection of irrigation events (mostly linked to missing precipitation events in the initial datasets, which are then classified as irrigation) is expected to be mitigated. New SM products are expected to be available in the near future, such as the SMOS-HR mission  (Rodríguez-Fernández et al. , 2019)  and ROSE-L mission  (Pierdicca et al. , 2019) , which could provide access to higher spatial resolution SM observations and allow for combination of multiple SM products from different sources, which in turn will increase temporal and spatial availability of this variable. The advent of these new products will allow for a more precise estimation of irrigation amounts at the field level.
Acknowledgements The first author received the grant  DIN2019-010652  from the  Spanish Education Ministry (MICINN)  and  DI-2020-093  from the  Catalan Agency of Research (AGAUR). We are grateful for the financial support given by the  ACCWA  project, funded by the  European Commission   Horizon 2020 Program for Research and Innovation , in the context of the  Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE)  action under the grant agreement No. 823965 , and by the  PRIMA ALTOS  project (No. PCI2019-103649 ) of the  Ministry of Science, Innovation and Universities of the Spanish government. The  PRIMA IDEWA  project is also acknowledged.
The JMP program (JMP® Pro 13. 0. 0) was used to perform the statistical analysis. In order to evaluate the efficiency of the BBD models of each output for the two processes, statistical criteria were used to evaluate the performance of the models with a confidence level of 95%. An analysis of variance (ANOVA) was conducted to determine the statistical capabilities of the generated models. Several variables and tools were considered to assess model fit, including probability (P-value), Fisher-test (Fvalue), Coefficient of Variance (C. V), Coefficient of Determination (R 2 ), Adjusted Coefficient of Determination (R adj 2 ), and Root Mean Square Error (RMSE) for the BBD models  [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]. The F value indicates the variation in responses, which can be assessed by a regression equation.
The BBD analysis The main objective of the experimental design is to determine, with a minimum number of experiments, the effects of different parameters or factors Xi and their interactions on a process variable of interest Y (yield), called the response. In this work, the optimization of SG food dye degradation was performed on two complexes, namely (Fe(III)/Lig) and (Fe(II)/Lig), using the BBD approach. In both cases, the impacts of various factors, including the initial concentration of iron (0. 15-0. 25 mM), the molar ratio between the ligand and iron (1-3), and the dye concentration (10-20 mg/L), were investigated. In the case of the (Fe (II)/Lig) complex, an additional independent parameter, the H 2 O 2 concentration (0. 05-0. 15 mM), was incorporated. Statistical analysis of the data was conducted using the JMP software (version 13 pro).
Interface for optimization and prediction In pursuit of delivering a practical solution for the implementation of multi-objective optimization (MOO) and the prediction of key parameters such as R%, COD%, and processing efficiency for various complexes, we have developed a user-friendly interface utilizing MATLAB's guide framework. This interface, as illustrated in Fig. 8 , has been further transformed into an executable application tailored for the Windows operating system. This versatile application presents a valuable and accessible means to forecast outcomes by allowing users to select input values corresponding to the specific type of complex defined by the BBD. It empowers users to discover a MOO solution by identifying the optimal input values for each complex, achieved through the utilization of the MOGWO algorithm.
Fluxes and meteorological datasets This analysis utilized eddy covariance fluxes and meteorological data collected at the 44 European ecosystem flux tower stations (Fig. 1 ), which can be accessed through the Integrated Carbon Observation System (ICOS) Carbon Portal (Warm  Winter 2020 Team and ICOS Ecosystem Thematic Centre, 2022). These stations are located in European countries and represent nine plant functional types, including closed shrubland (CSH), deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), grassland (GRA), mixed forest (MF), open shrubland (OSH), savannah (SAV) and wetland (WET). The dataset thus covers the main European geographical areas, and the dominant plant functional types present in Europe, with the exception of croplands, which were excluded from the analysis due to the year-to-year variability in crops within pluri-annual rotations, making the detection of GPP anomalies difficult to discern.
Air pollution datasets To determine ecosystem exposure to atmospheric pollution, we used hourly air pollutant concentration data simulated by a regional-scale chemical transport model (LOTOS-EUROS)  (Manders-Groot et al. , 2016) , which had a spatial resolution of 0. 1 • × 0. 1 •. The simulated pollutants included gaseous species such as ozone (O 3 ), nitrogen dioxide (NO 2 ), nitric oxide (NO), ammonia (NH 3 ) and sulphur dioxide (SO 2 ), as well as particulate matter (PM), which consisted of secondary inorganic aerosol smaller than 2. 5 μm (SIA 2. 5 ), particulate matter smaller than 10 μm (PM 10 ) and particulate matter smaller than 2. 5 μm (PM 2. 5 ). The LOTOS-EUROS model has various sources of uncertainties related to its different components such as emissions, chemical conversions, chemical interactions between species, and deposition  (Manders et al.
Diffuse and total radiation To compensate for the lack of full radiation instruments at some study sites, we utilized solar radiation components (global SW in , direct SW dir and diffuse SW dif ) from the Copernicus Atmosphere Monitoring Service (CAMS) radiation service. These data were obtained at 15-min intervals for all-and clear-sky conditions  (Gschwind et al. , 2019; Lefèvre et al. , 2013; Qu et al. , 2017). UTC date and time were converted to the local time, and half-hourly irradiance data were calculated by averaging the 15-min measurements. The diffuse radiation fraction (DF) was then determined as the ratio of diffuse to global irradiance on the horizontal plane at ground level. Both the CAMS all-sky and clear-sky radiation estimates were subjected to rigorous input quality control, regular benchmarking against ground stations, and regular monitoring to ensure consistency and detect possible trends  (Schroedter-Homscheidt et al.
Multiple linear model and simulation To investigate the causes of the spring 2020 GPP anomalies, we constructed multivariate models based on data for potential environmental drivers from the reference period and compared the simulated estimates to observed values. Using meteorological variables (SW in , SW dif , T a and VPD) obtained at each site, we built a multivariate model of GPP with a least square regression. We identified the most parsimonious linear model of GPP by applying information criterion techniques via the dredge function of the "MuMIn" package in R  (Bartoń, 2020; Tang et al. , 2018). The dredge algorithm generates all possible univariate and multivariate models of the response variable (GPP) based on predictors (meteorological and/or pollutant variables), and selects the model with the minimum value of the Akaike information criterion (AIC) -the model with the lowest value of the likelihood function by number of model parametersas the most parsimonious  (Akaike, 1976).
We then used this model trained on the reference period to predict the 2020 GPP at a daily time scale. In addition to R 2 , we used the normalized root-mean-square error (NRMSE) values computed by the MATLAB "compare" function to assess the model's fitness by indicating how well the predictions or simulations matched the observed data  (MathWorks, 2022). The NRMSE ranges from -∞ to 100 %, where 100 % indicates a perfect fit, -∞ indicates a poor fit and a fitness value of zero indicates that the identified model is no better than a straight line at matching the observed output.
76, NRMSE > 51 %) (Table  S4 , Fig. S7c-d ). By using a relevant or larger dataset that includes more historical information, our models were better able to account for this anomalous warming effect and provide more accurate predictions of GPP. These findings highlight the importance of incorporating relevant and comprehensive data when modelling ecosystem processes. The Group 3 sites (FI-Hyy, BE-Dor, CH-Cha, DE-RuR, ES-Abr, ES-LM1 and ES-EM2) were subjected to a range of management practices such as thinning, cutting, fertilizer application, grazing and nutrient manipulation experiment  (El-Madany et al. , 2021; Gharun et al. , 2020) , which confounded the patterns of GPP. For instance, intensive thinning resulted in a substantial decrease in LAI, and consequently reduced light interception by the canopy at FI-Hyy  (Aslan et al.
Acknowledgements We acknowledge the utilization of eddy covariance data obtained and shared by the  ICOS research infrastructure  and site investigators. The processing and harmonization of the eddy covariance data were conducted by the  ICOS Ecosystem Thematic Centre. ACI Tang was funded by the  Department of Ecology and Biodiversity (ECODIV) of INRAE. We also acknowledge the support provided by  INRAE  for the execution of this study. We thank  Tom Taborski  for his assistance in downloading and processing the CAMS radiation data. DP thanks the support of the  PRIN 2020 MULTIFOR  project (prot. 2020E52THS ). Lastly, we would like to pay tribute to the late  Werner Eugster  for his invaluable contributions.
Statistical analyses All the statistical analyses were performed with the R Statistical Software (v4. 2. 2). Briefly, we used the kruskal. test function for Kruskal Wallis tests, cor. test function Pearson correlation, the car package for calculation of heritabilities and covariance analysis and ggplot2 package for plotting graphs. Broad-sense heritability was calculated as H 2 = (st 2 -se 2 )/st 2 , where st 2 is the total variance observed over all the genotypes from the RIxGW progeny and se 2 the residual variance extracted from the anova of the RI and GW elementary plots in experiment A.
15. 1)  45  and then fetched into Stacks v2. 60  46  , using the modules "gstacks" and "populations" to produce a vcf file containing the variants detected across individuals. The obtained vcf file was then filtered using bcftools (version 1. 9)  45  , removing loci with average missing data > 10%. Samples with the remaining loci had a maximum of 30% of missing data (and only 18 samples had a missing data level > 10%, among which only 5 had a level > 20%) and were all kept for the subsequent steps. The two parental genetic maps were built using Lepmap3 47. Briefly, the final vcf file was submitted to the module ParentCall2 together with a pedigree file, to call segregating markers. The call file went then through the module SeperateChromosomes2 to split the markers over linkage groups. Nineteen linkage groups with confident support were retained and the markers were ordered on each linkage group using the module OrderMarkers2.
Thirty runs were performed for each linkage group and the best run based on likelihoods was retained for each. For parental maps, the option "informativeMask=13" or "informativeMask=23" in the module SeperateChromosomes2. The parameter « grandparentPhase=1 » in module OrderMarkers2 allowed to obtain phased data that was converted to fully informative "genotype" data by the script map2gentypes. awk. The parental genotypes are always "1 2" and the data is phased so that the first digit of the genotypes of the progeny is inherited from the male parent and the second from the female parent. This means that the raw genotypes of the progeny after the conversion were "1 1", "1 2", "2 1", or "2 2" with the first digit inherited from the GW (male) parent. The parental genetic maps are equivalent to a backcross type map.
Gewurztraminer susceptibility to esca is governed by a single dominant factor located on grapevine chromosome 1 To decipher the genetic basis of the observed variations in white rot and total necrosis, a quantitative trait locus (QTL) analysis was performed in RIxGW population. To this end, we used genotyping-bysequencing (GBS) data acquired on a set of 252 individuals of the progeny to establish two parental genetic maps. Both maps cover 19 linkage groups, corresponding to the 19 chromosomes of V. vinifera, and a high marker density, with an average distance of 0. 1 cM between markers (Table  3 , Figure  S2 ). The female (RI) map includes 9 449 SNPs, with a total genetic length of 1239 cM. The male (GW) map has 9 427 SNPs covering 1175 cM. QTL detection was performed for all the measured traits.
Acknowledgments We are grateful to  C. Rustenholz  and  A. Velt  for providing Gewurztraminer sequences used to develop the KASP marker, to the  Vignes Vivantes  association for performing the vine trunk sections and the Gentyane platform for KASP analyses. We would like to thank  P. Mestre  for his careful reading which helped improve the manuscript. We also thank our colleagues of the  Unité Expérimentale Agronomique et Viticole INRAE-Colmar  for maintaining the vineyard plots and  E. Chedid  for her advice on QTL analysis. The data underlying this article are available in the article, in the supplementary information files and in the online supplementary material at https://doi. org/10. 57745/BWY9QK. 00.
, 2022 , 2023; Serrano-Martínez et al. , 2021; Serrano-Martínez, Knoll, Narváez, Lautenschlager, & Ortega, 2019; Serrano-Martínez, Knoll, Narváez, & Ortega, 2019) , thalattosuchians  (Brusatte et al. , 2016; Fernández et al. , 2011; Herrera et al. , 2018; Pierce et al. , 2017; Schwab et al. , 2021; Wilberg et al. , 2021) , early crocodylomorphs  (Leardi et al. , 2020; Melstrom et al. , 2022; Ruebenstahl et al. , 2022)  and some notosuchians  (Dumont Jr et al. , 2020; Fonseca et al. , 2020; Kley et al. , 2010; Pochat-Cottilloux et al. , 2021; Sereno & Larsson, 2009). Those studies are based on extant crocodylian databases for comparison  (Bona et al. , 2017; Witmer et al. , 2008) , as well as detailed studies focusing on certain parts of the braincase  (Dufeau, 2011; Dufeau & Witmer, 2015; George & Holliday, 2013; Hu et al.
, 2020; Larsson & Sues, 2007; Pochat-Cottilloux, Perrier, et al. , 2023; Rauhut & Lopez-Arbarello, 2005). Here, we describe the internal and external anatomy of a new specimen of Hamadasuchus (UCBL-FSL 532408) from the late Albian-Cenomanian Kem Kem group of Morocco. Using the reconstructed internal structures of this specimen (cranial endocast, endosseous labyrinths, and braincase pneumaticity), we use several proxies to infer associated paleobiological traits of Hamadasuchus, putting them into perspective with comparative data on sebecosuchians, eusuchians, and thalattosuchians. The data are also used to reconstruct the braincase osteology, allowing a robust taxonomic assessment of this specimen.
| Paleobiological inferences Brain volume was estimated using the following relation: log BV = log EV * 0. 7279 + 0. 75624 where EV: endocranial volume and BV: brain volume  (Dumont Jr et al. , 2020  and references therein; see also Data S1). The volume of the sinus system was calculated using Blender v2. 91 and skull width was measured as the largest skull width (including quadrates; see Data S1). We chose to use the skull width rather than the skull length to prevent us from potential convergence problems related to the size of the rostrum, as well as making it possible to include fossil specimens for which the skull length is unknown without having to estimate it.
but, as rightly highlighted in Dumont Jr et al. (2020) , this use is problematic because the REQ changes throughout ontogeny (corresponding with changes in brain morphology), even between different adult stages (Hurlburt et al. , 2013). As a result, we completed the dataset of Dumont Jr et al. (2020; Figure 8), which rather uses the raw log-transformed measurements of body and brain mass (Figure 8; see also Data S1). Body mass is estimated from skull F I G U R E 7 Three-dimensional reconstructions of the endosseous labyrinths of different reptiles in lateral view: (a) Hamadasuchus (right, UCBL FSL 532408); (b) Zulmasuchus querejazus (left, MHNC 6672 from Pochat-Cottilloux et al. , 2021); (c) Crocodylus porosus (left, OUVC 10899); (d) Viavenator exxoni (left, MAU-Pv-Li-530 from Paulina-Carabajal & Filippi, 2018); (e) Baurusuchus sp.
(i. e. , longirostrine Gavialis and brevirostrine Caiman have similar sinuses;Pochat-Cottilloux et al. , 2021, figure  8). Finally, there seems to be a link between the development of the sinus system and the living environment in crocodylomorphs, as terrestrial forms, such as Zulmasuchus, have a very expanded cranial pneumaticity, whereas among forms showing adaptations to the aquatic environment, such as thalattosuchians, cranial pneumaticity is, as stated above, reduced. To get a better view of the problem, we introduced the data obtained from the current study on Hamadasuchus in a dataset consisting of extant specimens of diverse ecologies, as well as fossil representatives such as Diplocynodon, Trilophosuchus, Portugalosuchus, or Zulmasuchus, all adults (see Data S1 for more information).
Taxon Specimen Publication or database Alligator mississippiensis UCBL WB35 This study Caiman crocodilus UMMZ HERPS 128024 Morphosource Crocodylus acutus MZS Cro 055 This study Gavialis gangeticus MLP 602, UF HERP 118998 Bona et al. (2017), Pierce et al. (2017), Morphosource Mecistops sp. MHNL 50001393 This study Osteolaemus tetraspis UCBL 2019-1-236 This study Paleosuchus trigonatus MHNL 50003939 This study Tomistoma schlegelii TMM M6342 Morphosource Tomistoma downsoni NHMUK PV R 4769 Burke and Mannion (2023) Diplocynodon tormis STUS-344 Serrano-Martínez, Knoll, Narváez, and Ortega (2019) Arenysuchus gascabadiolorum MPZ 2011/184 Puértolas-Pascual et al. (2022) Agaresuchus fontisensis HUE-02502, HUE-03713 Serrano-Martínez et al. (2021) Agaresuchus subjuniperus MPZ 2012/288 Puértolas-Pascual et al.
Sampling Data were obtained during the oceanographic cruise Camadas Finas III, aboard the research vessel NHo. Cruzeiro do Sul-H38 (DHN/Brazilian Navy). It was performed in 9-31 October 2012, the period when most of the ARP is retroflexed and transported eastward by the NECC  [43, 45]. Zooplankton was sampled at 44 stations in oblique hauls, using Bongo nets with 120 and 300 μm mesh and 0. 3 and 0. 6 m mouth diameter, respectively. Tows were performed at approximately 2 knots from near bottom to surface on the continental shelf, and from 200 m to the surface in the open ocean. Nets were fitted with a flowmeter (Hydro-Bios) to estimate the filtered volume. Samples were fixed with 4% formaldehyde buffered with sodium tetraborate (0. 5 gL -1 ). Current speed and directions were recorded along all the track of the ship by a Teledyne RD Ocean Surveyor ADCP.
Data analysis The ARP was delimited by the isohaline of 35 and density <22 (σ T ). Tropical Surface Water (TSW) and South Atlantic Central Water (SACW) masses were delimited by the isobar of 24. 5 (σ T )  [43, 54, 55]. Cyclonic and anticyclonic eddies were respectively identified by negative and positive sea levels anomalies in daily L4 satellite data, measured by multi-satellite altimetry observations over Global Ocean, produced by SSALTO/DUACS and distributed by European Union Copernicus Marine Service Information. Dominant current in each station was determined by the overall direction observed in the ADCP data and classified as NBC, retroflection area (RETRO) and NECC. The abundance, species richness and structure of the planktonic cnidarian assemblage was statistically similar in samples from nets with 120 and 300 μm meshes  [56].
Thus, we merged the data from both meshes for statistical analysis. Station 1, the was the only spot sampled inside the river estuary and was excluded from the data analysis. For statistical tests, abundance data was transformed by log (x+1). Analyses of Variance (ANOVA) were performed to test for differences in abundance of the most abundant planktonic cnidarian species, according to the province (neritic and oceanic) and influence of the ARP. Tukey post-hoc test was used to identify the domains that differed when ANOVA was significant. Spatial patterns in planktonic cnidarian assemblage abundance were identified by a hierarchical cluster analysis (Bray-Curtis similarity matrix). A Similarity Percentage (SIMPER) analysis was performed in order to identify key species and their contribution to similarity within the groups defined in the cluster analysis.