dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation metrics for segmentation For the evaluation of networks segmentation results in 3D, we used some common objective criteria including geometrical metrics such as Dice score (DSC) Eq. (  4 ) for the different areas of interest and Hausdorff Distance (HD) Eq. (  5 ) for the myocardium and clinical metrics including relative volume errors, i. e. , Absolute Volume Difference (AVD) Eq. (  6 ) and Absolute Volume Difference Rate according to the volume of the myocardium (AVDR) Eq. (  7 ). [44] [45] [46] [47] [48]. The evaluated metrics were based on the official code https://github. com /EMIDEC -Challenge /Evaluation -metrics. The DSC computes the spatial overlap of the network-generated boundary and manual contouring and is determined as follows: D SC = 2 × (N ∩ M) N + M (4) (Where N and M designate the corresponding network and manually segmented tissues.
Evaluation results of the proposed model on EMIDEC dataset There are a total of 100 cases with published labels to train, validate, and test our network's performance. We make random 5-fold cross-validation by shuffling the scan sequence and splitting the EMIDEC dataset into 5 folds using 68 MR scans for training. Table  2  and Table  3  represent the cross-validation results of our segmentation output before the application of postprocessing removal of correlated errors in myocardial disease segmentation from LGE-MRI data and two methods segmentation output. Table  2  gives an extensive evaluation of the conducted experiments' metrics on the 5-fold cross-validation. Our approach proves that with the restricted low number of training samples, it is conceivable to segment LGE-MRI scans. It can be observed that the proposed method can accurately segment the anatomical structures as well as the pathological tissues.
Validation of the performance of the proposed anatomical model on benchmark datasets The proposed method (anatomical model) has been evaluated and tested on other publicly available LGE-MRI datasets such as Myocardial pathology segmentation combining multi-sequence CMR (MyoPS) 2020 dataset in MICCAI 2020 challenge and a Multisequence Cardiac MR (MS-CMRSeg) 2019 in MICCAI 2019. The data has been preprocessed based on the multivariate mixture model (MvMM)  [49]. The performance comparison of our proposed anatomical method and existing deep learning models on  Each row displays a case on two different heart locations, followed by a 3D surface rendering. The rows from top to bottom are the ground truth, the proposed method prediction, and 3D U-Net segmentation.
Acknowledgements. We would like to thank the two referees,  Evan Gowan  and  Javier Blasco , for their insightful comments. Catherine Ritz  is warmly thanked for her dedication to the GRISLI ice sheet model. We acknowledge the  Institut Pierre Simon Laplace  for hosting the iLOVECLIM model code under the LUDUS framework project (https://forge. ipsl. jussieu. fr/ludus,last access: 8 October 2021). Financial support. The research leading to these results has received funding from the  SCOR foundation project COASTRISK.
The present paper proposes the numerical solution of an inverse problem in groundwater flow (Darcy's equation). This solution was achieved by combining a high-resolution new code HYSFLO-LBM (Hydrodynamic of Subsurface Flow by Lattice Boltzmann Method), based on LBM, to solve the direct problem, and the metaheuristic optimization algorithm CMA-ES ES (Covariance Matrix Adaptation-Evolution Strategy) to solve the optimization step. The integrated optimization algorithm which resulted from this combination, HYSFLO-LBM/CMA-ES, was applied to the hydrogeological experimental site of Beauvais (Northern France), instrumented by a set of sensors distributed over 20 hydrogeological wells. Hydrogeological parameters measured by the sensors are necessary to understand the aquifer functioning and to serve as input data for the identification of the transmissivity field by the HYSFLO-LBM/CMA-ES code.
For instance, suppose we know f 7 and ϕ on the boundary denoted ϕ wall , then expression  (19)  allows us to transform the value of ϕ wall into f 5 by: f (eq) i -f i = f opp(i) -f (eq) opp(i) f (eq) 5 -f 5 = f opp(5) -f (eq) opp(5) = f 7 -f (eq) 7 = f 7 -ω 7 ϕ wall or f 5 = ω 7 ϕ wall + ω 5 ϕ wall -f 7 (20) Finally, we give the Algorithm 1 implemented in the computer code HYSFLO-LBM to solve the Darcy equation, which was used for the direct problem. 14 )) loop: t + ∆t compute λ ij from (Equation (  17 )) compute f (eq) k from (Equation (  14 )) collision from (Equation (  15 )) streaming from (Equation (  16 )) set boundary condition from (Equation (  19 )) compute ϕ from (Equation (  18 )) test ϕϕ 0 < tol exit set ϕ 0 = ϕ go to the next time step.
On the other hand, water levels have been recorded by the piezometric manual probe SOLINST and by automatic datalogger (CTD DIVER, developed by Schlumberger water Service  [43]  and designed to measure, especially, water pressure), which contains electrical conductivity and temperature sensors, memory for storing measurements, and a battery. Recorded data are afterwards stored in the DIVER's internal memory. Indeed, the DIVER consists of a pressure sensor, designed to measure water pressure, and a temperature sensor. The DIVER contains an autonomous datalogger and the duration of the measurements can be chosen by the user, ranging from seconds to hours. The various recorded measurements can be recovered either in the laboratory or at the measurement site using an optical communication system linking DIVER to a laptop computer.
Modeling of the Hydrogeological Experimental Site of Beauvais system requires expertise in terms of hydrodynamic characteristics, water table levels, the computed hydrological balance, transmissivity, and hydraulic conductivity. Generally, these parameters result from geostatistical concepts, laboratory experiments, pumping tests  [44, 45] , and magnetic resonance sounding (MRS) for their hydrogeologic estimation  [46]. Indeed, MRS investigations summarize the definition of the hydrodynamic parameters, especially in Beauvais city (in the North Paris Basin) and near the Hydrogeological Experimental Site, and will serve to ameliorate the database which should be used in the numerical processes of this paper and contribute to the knowledge of the heterogeneous permeable bodies  [47]  in the chalk aquifers.
Acknowledgments: The identification of transmissivity values will help us to know the hydrodynamic function of the chalk aquifer in the Oise region, especially in the  Hydrogeological Experimental Site of Beauvais  (HESB, northern part of the Paris basin). The construction of this site has benefited from the support of the  European Regional Development Fund (FEDER) , the  Picardy region (Haut-de-France) , and the  Ministry of Higher Education and Research. This site and its hydrogeological platform are attached to the  AGYLE Research team "Agroecology, Hydrogeochemistry, Environments, and Resources" of the Institut Polytechnique UniLasalle.
Acknowledgments: The identification of transmissivity values will help us to know the hydrodynamic function of the chalk aquifer in the Oise region, especially in the  Hydrogeological Experimental Site of Beauvais  (HESB, northern part of the Paris basin). The construction of this site has benefited from the support of the  European Regional Development Fund (FEDER) , the  Picardy region (Hautde-France) , and the  Ministry of Higher Education and Research. This site and its hydrogeological platform are attached to the  AGYLE Research team "Agroecology, Hydrogeochemistry, Environments, and Resources" of the Institut Polytechnique UniLasalle.
Acknowledgements. The authors thank  Sven Baars  for insightful discussions. Pascal Wang acknowledges the hospitality of the  Institute for Marine and Atmospheric research Utrecht  where the scientific work was conducted. Financial support. This research has been supported by the  European Commission, H2020 Research Infrastructures (CRITICS  (grant no. 643073 )). Review statement. This paper was edited by  Balasubramanya Nadiga  and reviewed by two anonymous referees.
P. Wang et al. :  Improvements to the use of TAMS Code availability. The software is available at https://doi. org/10. 6084/m9. figshare. 13914353. v2 (Wang, 2021a) and https://doi. org/10. 6084/m9. figshare. 13919642. v1 (Wang, 2021b). Data availability. Datasets for Figs. 6 and 8 are available from Wang and Castellana (2021, https://doi. org/10. 6084/m9. figshare. 13918100. v1).
2020). If many, and among them the earliest, formalised SD procedures are mostly carried out manually, e. g. by comparing trees with the geographic occurrence of individuals, calculating correlations between geographic and genetic distances, assessing steepness of hybrid zones, or seeking for correlation between genetic distance and morphological characters  (Good & Wake 1992 , Wiens & Penkrot 2002 , Vieites et al. 2009 , Flot et al. 2010 , Weisrock et al. 2010 , Puillandre et al. 2012a , Miralles & Vences 2013 , Derkarabetian & Hedin 2014 , Dufresnes et al. 2015) , a substantial number of computer-based tools has been developed to delimit species, often based on statistical criteria. These programs can analyse large datasets, with a strong focus on the use of sequence data (Table  1 ). These methods have significantly contributed to increase the objectivity, repeatability, and speed of species delimitation inferences under different mathematical models and assumptions (e.
g. Multispecies coalescent model, DNA barcode gap, haplotype fields of recombination, cf. de  Queiroz 1998, 4  2007,  Knowles & Carstens 2007 , Yang & Rannala 2010 , Carstens et al. 2013 , Leavitt et al. 2015 , Rannala 2015). Although the number and importance of SD tools is likely to sharply increase in the immediate future, they are not yet routinely used in the majority of alpha-taxonomic studies that result in the naming of over 15,000 new species of organisms every year  (Miralles et al. 2020). One obvious shortcoming is the lack of interoperability among the various independently developed SD programs, and the lack of comprehensive software suites that offer various user-friendly features, such as those for data visualization and comparison of results across methods. For instance, incongruent species partitions resulting from different SD approaches applied to a given dataset are common.
1 )), but all of them match the aforementioned definition of a subset. Furthermore, while some tools produce de novo species partitions (i. e. directly aggregating individuals into species hypotheses; exploratory methods), others statistically compare competing species hypotheses that have been defined a priori (hypothesis-testing methods), and these programs require a species partition as input. SD methods may also assign scores, either to the entire inferred partition (e. g. , ASAP-score in the program ASAP;  Puillandre et al. 2021) , to the distinctiveness of each subset from the others (e. g. , posterior probabilities in the programs BPP and bPTP;  Yang & Rannala 2010; Zhang et al. 2013) , or to the presence of each individual in a given subset (e. g. , probability of placement in calculation of BINs,.
A standardized Species PARTition format (SPART) Typically, each SD program exports the resulting species partitions in its own idiosyncratic format. Some, for instance, provide a table of assignments of individual specimens to the subsets (e. g. GMYC) while others, conversely, list the different subsets with the included individuals (e. g. ABGD, PTP), whereas again others graphically report subsets on a tree topology (e. g. GMYC). These different formats may or may not include complementary data (e. g. , scores, topologies, metadata, number of species delimited, etc. ), and are not designed to be parsed by other tools for downstream analyses. Their manual conversion into a versatile and easily reusable plain text species partition (e. g. , CSV) is not always straightforward. It can be particularly error prone and time consuming with large datasets, as species delimitations on several hundreds, or even thousands, of specimens are becoming common practice in molecular taxonomy (e.
g. ,  Ahrens et al. 2016 , Renner et al 2017 , Garcià-Melo et al. 2019 , Hoffmann et al. 2019 , Solihah et al. 2020 , Christodoulou et al. 2020). We here propose a standardized species partition format, SPART, to enable compatibility between different tools producing (export) or using (import) species partitions. Our format facilitates: (1) statistical comparison of different alternative species partitions such as their overall congruence, similarity or resolving power, identification of the subsets that are congruently delimited (currently implemented in the program LIMES v2. 0;  Ducasse et al. 2020 ); (2) assessment of multiple competing SD hypotheses, including those used as input in e. g. DELINEATE and BPP to evaluate them  (Sukumaran et al. 2020 , Yang & Rannala 2010 ); (3) visualization and comparison of species partitions (e.
g. , DNA-based species partitions compared with manually-edited species partitions obtained from alternative methods and data such as Principal Component Analysis of morphometry, haplotype networks, geographic distribution, habitat type, external phenetic similarity, or simply, current taxonomy); (4) extraction, from original data files, of specific data for each subset under different species partition assumptions (e. g. lists of molecular and morphological diagnostic character states, descriptive statistics characterizing each of the inferred species, or ecological or distributional traits); and (5) potential taxonomic reassignment of specimens in databases. More generally, the SPART format is designed to be versatile and fully integrative in the sense that it can include any species partition descriptors, independently of the method or data-type used to generate the species partition (Fig.
Matricial and serial implementation of the SPART format SPART files include information on one or multiple species partitions for a given set of elements (i. e. individuals) and use standardized terminology to denote the number of species partitions included in the file ("N_spartitions") and for each partition, the number of individuals ("N_individuals"), number of subsets ("N_subsets"), and the assignment of individuals to subsets ("Assignment") (Supporting information 1). The syntax also allows to optionally include support values for species partitions, subsets, and the assignment of individuals to subsets, as well as original trees and the full command line used in the respective SD analyses, the program version number as well as comments and species partition comparison indices as calculated with LIMES 2.
0, a new version of LIMES  (Ducasse et al. 2020 ) recently published. To account for the diversity of possible future applications, we propose two variants of the SPART format (for details see Supporting information 1). Both of these use largely the same terminology but represent the data differently: The first SPART variant is optimized for human readability and its syntax has been designed to be compatible with Nexus (a widely used data format in phylogenetic inference software:  Maddison et al. 1997). This allows to include SPART specifications as blocks in Nexus files if required by future applications. If information from multiple partitions is included, then it is combined into a single block, presenting the respective assignments and assignment scores per individual from different species partitions concatenated on a single line, separated by separator symbols.
This enables easy manual transformation into a spreadsheet format if required. Due to the presentation of information from multiple partitions in one block as a concatenated matrix, we denote this variant as matricial SPART format, or simply SPART. The second SPART variant is optimized for machine readability, and relies on XML (eXtensible Markup Language), a lightweight data-interchange format that can be easily parsed and written by software tools, while it can still be read and written by humans as well. When information from multiple partitions is included, each partition forms a separate block containing information on the number of subsets, individual assignments and assignment scores. We therefore denote this variant as SPART. XML format.
Tools already implementing SPART and future perspectives The proposed format is already implemented in several widely-used SD programs. Both the matricial SPART and SPART. XML output files are already generated by GUI-driven standalone versions (https://github. com/iTaxoTools) of ABGD, ASAP, GMYC, PTP, mPTP, TR2 and DELINEATE  (Vences et al. submitted) , by the native Python version of TR2, and in the web versions of ABGD and ASAP; and in progress for the Python versions of GMYC and PTP. Furthermore, the species partition comparison tool LIMES v2. 0 has been expanded to import, export and convert SPART files, in particular to (1) compare, by calculating indices (e. g. , Ctax, Ratx, Match Ratio, cf. Ducasse et al. 2020)  for species partitions from SPART files (including each one or several species partitions); (2) merge species partitions included in different SPART files into one SPART file, (3) import species partition(s) table(s) from spreadsheet editors such as Microsoft EXCEL and save it (them) into a single SPART file.
10 [note to reviewers: all of these new software versions will be released before or upon publication of this manuscript, and the web based ABGD and ASAP are already freely available since February 2021 (https://bioinfo. mnhn. fr/abi/public/abgd/abgdweb. html, https://bioinfo. mnhn. fr/abi/public/asap/) ; for review, various preliminary Windows executables are available under this link: https://hidrive. ionos. com/share/ohaymwcgjd#$/Win%20executables ] In the context of future work, we envisage the development of visualization tools to automatically illustrate information from species partitions along with support values and phylogenetic hypotheses (Fig. 1 ). There is still a long way to go before programs will be able to infer species based on combining evidence using different data sources such as genetics, morphology, ecology, behaviour, geographic distribution, etc.
Acknowledgments We are grateful to  Susanne Renner  who stimulated this work by leading the priority program  SPP 1991 "Taxon-Omics" of the Deutsche Forschungsgemeinschaft (DFG) , specifically in the context of a grant on taxonomic data integration and management ( RE 603/29-1 ), and to many members of the  Taxon-Omics consortium and Guillaume Achaz (MNHN)  for fruitful discussion. MV and SK were supported by  DFG  grant  VE247/20-1 , NP by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 865101 ), and AS and SL by the  Klaus-Tschira foundation.
Models and observational references In this study, SITool (v1. 0) is used to evaluate the CMIP6 OMIP model skills in simulating the historical sea ice properties for both hemispheres. The CMIP6 OMIP models and a set of observational references providing ice concentration, thickness, snow depth, and ice drift are introduced in this section. Two sets of observational references for each sea ice variable are used for comparison. The CMIP6 OMIP models used are shown in Table  2  with model details such as atmospheric forcing, ocean models, sea ice models, spatial resolution, and related references. A major improvement in JRA55-do atmospheric forcing relative to the CORE-II forcing is the increased temporal frequency from 6 to 3 h and horizontal resolution from 1. 875 to 0. 5625 •. The surface fields of JRA55-do forcing have been adjusted to match reference datasets based on high-quality X.
In general, model errors are larger than observational uncertainty, and model performance on the ice concentration, extent, edge location, and ice drift simulations is consistent https://doi. org/10. 5194/gmd-14-6331-2021 Geosci. Model Dev. , 14, 6331-6354, 2021 when comparing to different observational references. For the ice thickness and snow depth evaluation, the rating scores are not consistent compared to different observational references, which is due to the limited observations and to the fact that different months were chosen for comparison during 2003-2007. This finding shows that sea ice thickness and snow depth estimates are still at an earlier stage of maturity compared to datasets of sea ice concentration or drift. The improvements of mean ice concentration simulations in the summer for both hemispheres by changing the atmospheric forcing and increasing the horizontal resolution are also identified in  Tsujino et al.
The Sentinel-2 satellite constellation produces 13-band multispectral images in the visible and infrared frequency range with a minimum spatial resolution of 100 m 2 and a high revisit frequency (several images per month). These characteristics are a good compromise for mapping wetland vegetation, in particular through multi-date classifications  (Rapinel et al. , 2019; Vrieling et al. , 2018). To overcome these obstacles, remote sensing is a pertinent tool for identifying and locating habitats and, more broadly, areas with conservation issues. In this context, this study aims to assess the contribution of Sentinel-2 satellite images to mapping habitats in the Loire estuary by (1) developing an exhaustive typology of habitats from ground reference data and by (2) testing the accuracy of single-date and multi-date Sentinel-2 image classification using the Random Forest algorithm.
A vegetation plot corresponds to the list of plant species inventoried inside areas with a floristically homogeneous composition  (Dengler et al. , 2008). The cover of each species is visually estimated and quantified according to the Braun-Blanquet's scale  (Braun-Blanquet, 1932). We used 1,248 phytosociological vegetation plots to obtain a representative sampling of the diversity of habitat types present in the study site (Fig. 1 ). A total of 989 phytosociological vegetation plots were collected from the bibliography (scientific publications, PhD theses, impact studies, vegetation maps …) and we collected in the field phytosociological vegetation plots for this study. This dataset is constituted of vegetation plots that were collected from 2010 to 2019. Older vegetation plots were not selected because of a too long-time gap between field and satellite data.
For plant communities with a high degree of stability (e. g. woodlands, forests and some types of meadows), we selected training areas using data up to the year 2010. On the other hand, for highly dynamic plant communities, we have only kept the plots collected during the year of acquisition of satellite images (e. g. annual salt marshes vegetation (A2. 551 -'Salicornia, Suaeda and Salsola pioneer salt marshes') or reedbeds on regularly submerged banks (C3. 27 (NIR -R)/(NIR + R) (B8 -B4)/(B8 + B4) Rouse et al. (1973) NDWI (NIR -SWIR1)/(NIR + SWIR1) (B8 -B11)/(B8 + B11) Gao (  1996 )  Dash and Curran (2004)  -'Halophile Scirpus, Bolboschoenus and Schoenoplectus beds'). In addition, during the pixel selection phase by photo-interpretation, we checked that the pixels viewed and the habitat categories identified in the typology were consistent so as not to take training samples in locations where the vegetation might have changed.
Software We used the R software (version 3. 6. 2) (R Development Core Team, 2019) for our analyses, with the packages 'randomForest' (version 4. 6-14)  (Liaw and Wiener, 2002) , 'rgdal' (version 1. 4-8)  (Bivand et al. , 2015)  and 'raster' (version 3. 0-7)  (Hijmans, 2015). The results were visualized in QGIS 3. 4. 8 (QGIS Development Team, 2019).