dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 2 2. É Le traitement d'images pour l'estimation du rendement de la vigne se base sur une des tâches suivantes : la classification d'images, la détection d'objets et la segmentation. La classification associe une classe à une image. La détection d'objet effectue une classification et une localisation, l'objet détecté étant entouré par une boîte englobante. La segmentation va plus loin en associant une classe pour chaque pixel de l'image. L'évaluation est effectuée en divisant la base de données en deux parties : une base d'apprentissage utilisée pour entraîner le modèle et une base de validation. Différentes métriques de performances sont calculées sur ces deux bases de données. La différence entre les performances d'apprentissage et de validation doit être minime (quelques pourcents) sinon cela indique un sur-ajustement (le modèle n'est pas applicable correctement sur de nouvelles images).
In this study, we used this network of sites to evaluate seasonal and annual NEE and gross CO 2 fluxes (gross primary productivity, GPP, total ecosystem respiration, R eco , and evapotranspiration, ET) simulated by 14 process-based DGVMs against in situ flux tower data. Simulations from the 14 DGVMs were taken from the TRENDY model intercomparison project (MIP)  [27] ; https:// sites. exeter. acuk/trendy) version 7, which contributed to the Global Carbon Project's annual Global Carbon Budget  [28]. Our primary objective was to evaluate:  (1)  whether the models could reproduce the observed annual net CO 2 flux (NEE) dynamics across this range of dryland sites. The final three objectives of our study were designed to diagnose the causes of any discrepancies in modeled dryland annual NEE. More specifically, we aimed to determine: (2) whether model-data discrepancies could be alleviated by running a DGVM with site-level forcing and vegetation and soil characteristics?; (3) which seasons, and which of the gross CO 2 fluxes, were responsible for model discrepancies in predicting dryland mean annual NEE and its variability, and whether the season and gross CO 2 flux responsible was different for high elevation forested sites versus low elevation shrub and grassland sites?; and (4) whether incorrect model sensitivity to climate drivers, and particularly those related to moisture availability, is causing the discrepancies in the key seasons and gross CO 2 fluxes identified in (3)? The analysis associated with the last objective allows us to evaluate which C cycle related processes may need improvement before DGVMspt can be used for reliably predicting the role of dryland ecosystems in the global C cycle.
In situ CO 2 and water flux data processing and analysis Eddy covariance flux tower instruments at all sites collect half-hourly measurements of surface energy fluxes and NEE. NEE was partitioned into GPP and R eco by each site PI. Gross CO 2 fluxes (GPP and R eco ) were calculated from the net CO 2 flux using the relationship between nighttime NEE and temperature  [24]. Note that in this study a negative NEE implies a net CO 2 uptake into the ecosystem. Eddy covariance latent heat flux data were processed to provide evapotranspiration (ET). ET gaps were filled using a modified look-up table approach based on  [32] , with ET predicted from meteorological conditions within a 5 d moving window. We calculated seasonal CO 2 and water fluxes by summing the daily fluxes for the following months: November to February inclusive for the cool (winter) period; March to June hotter pre-monsoon (spring) period; and July to October for the monsoon.
Note that the spring could be further split into the warm, moist months of March and April followed by the hotter, drier months of May and June; however, this entire period is marked by relatively warm and dry, moisture limited conditions compared with the winter or monsoon. To determine which seasons and gross CO 2 fluxes may be responsible for underestimate in NEE IAV, we examined the R 2 values obtained from the linear regression between the observed annual and seasonal C fluxes and the annual NEE. [34]  and net transitions from the HYDE (History Database of the Global Environment) v3. 2  [35]. Each modeling group has its own protocol for merging this LUC data with their own PFT descriptions. Individual modeling groups also use their own expert judgment for setting their model parameter values, other required forcing data streams, and soil texture and permeable depth information.
54] from different dryland ecosystems worldwide. Modelers should also collaborate more extensively with empirical scientists to make best use of existing data and to collect or derive new dryland C cycle related datasets where needed. Once this cycle of model hypothesis testing and development is complete, we may find that dryland regions play an even greater contribution to global C cycle IAV than previously thought. (CLAND). AK was supported by Oak Ridge National Laboratories (ORNL). ORNL is managed by UT-Battelle, LLC, for the DOE under Contract DE-AC05-1008 00OR22725. Partial funding for some of the AmeriFlux sites run by RLS and MEL was provided by the US Department of Energy's Office of Science, NSF funding to the Sevilleta LTER program, and the USDA. USDA is an equal opportunity employer.
PK and TM were funded by the NOAA OAR/ARL Climate Research Program. SS was supported by NERC Driving-C project (NE/R00062X/1). SZ was supported by the European Union's Horizon 2020 research and innovation program under the Grant Agreement No. 821003 (4C-project). The CESM project is supported primarily by the National Science Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi:10. 5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. DLL was supported by the U. S. Department of Agriculture NIFA Award 2015-67003-23485. We would like to thank the ORCHIDEE Project Team for developing and maintaining the ORCHIDEE code and for providing the ORCHIDEE version 2.
) Caractérisation des étiages et de la pluviosité sur les bassins a. Données utilisées Les valeurs de débit sont issues des stations hydrométriques, dédiées à l'observation des étiages, gérées par Avignon Université et l'UMR ESPACE. Nous en utiliserons les débits moyens journaliers (de 0h00 à 24h00 TU) calculés à partir de mesures au pas de temps de 5 minutes. Pour le calcul des débits d'étiage (pris au sens large), nous avons considéré la moyenne des débits journaliers des trois derniers jours d'un cycle. Les données pluviométriques proviennent des bases de données de Météo-France et de la DREAL (voir Fig. 1 ). Treize points de mesure se trouvent sur le terrain d'étude et neuf autres à son voisinage (moins de 20 km).
La zone d'étude correspond au Massif Central selon le contour officiel donné par la Loi Montagne de 1985. En 2010, ce territoire comptait 72 408 exploitations, parmi lesquelles 2% avait un atelier porcin. En 2018, 4,4% de la production de porcs charcutiers du pays en est issu (source BDPORC). Le caractère très minoritaire des élevages porcins dans le Massif, et pour la production nationale, fait qu'ils ont été peu documentés. Les informations les plus récentes, actualisées en continu, sont fournies par la base de données professionnelle d'identification porcine BDPORC dans laquelle sont notifiés tous les mouvements de porcs entre lieux, ceux-ci pouvant être des sites d'élevages, des centres de rassemblement, des abattoirs et même des camions lorsqu'il y a transfert d'animaux d'un véhicule à un autre.
BDPORC est une base de données créée en 2006, qui a pour vocation de recenser et valoriser des informations concernant l'identification des sites d'élevage et d'abattage porcins, et d'autres informations d'ordre sanitaire ou relatives aux démarches qualités et mouvements d'animaux. 3 https://www. charte-origine-montagne. com/qui-sommes-nous/association-porc-montagne/ 2/ Méthode.
De la production de connaissances à l'évaluation de l'état des connaissances Nous sommes dans une situation où la communauté scientifique s'est approprié cet enjeu du changement climatique, dans toutes les disciplines scientifiques, aussi bien dans les sciences du climat, abordées précédemment, pour comprendre le fonctionnement du système climatique et les évolutions futures physiquement possibles, mais aussi pour comprendre les impacts et les risques potentiels du changement climatique, pour les écosystèmes, les secteurs d'activité, et les sociétés humaines, dans les différentes régions du monde, et enfin pour explorer les options d'action, pour la gestion de risque, pour l'adaptation, et pour réduire les rejets de gaz à effet de serre, tout en assurant un développement soutenable. Une recherche dans les bases de données internationales des publications dans les journaux à comité de lecture montre plus de 20 000 publications scientifiques annuelles avec le mot clé « climat »(Figure  8 ).
ACKNOWLEDGEMENTS We thank  C. Bens  and  A. Verguin  of the Collections de Pièces anatomiques en Fluides of the  MNHN de Paris. The MNHN gives access to the collections in the framework of the  RECOLNAT National Research Infrastructure. This work was supported by a grant from  Agence Nationale de la Recherche  in the LabEx  ANR-10-LABX-0003 - BCDiv, program 'Investissements d'avenir'  no. ANR-11-IDEX-0004-02. We thank  B. Wilhelm  and an anonymous reviewer for their helpful comments. A. Hu. and R. M. performed the dissections and measurements for the pectoral and pelvic fins, respectively. A. Hu. and R. M. interpreted the results and wrote the manuscript. A. He. and M. H. designed the research and revised the manuscript. The authors have no conflict of interest to disclose.
Field procedures and survey protocols The permanent natural (streams, rivers) and artificial (ponds) water bodies occurring in the study area were represented into a random-origin grid of 606 cells of 1 × 1 km using Quantum GIS v. 2. 14. 3 (QGIS Development Team 2016), among which 150 cells were randomly selected for surveying (Figure  1 ). A further selection was conducted using aerial photographs to exclude cells with unsuitable areas for SWV, defined as dense riparian woodland and urbanised areas without riparian vegetation. During the first field prospection in March 2016, hygrophilic riparian vegetation, i. e. the suitable habitat for SWV  (Pita et al. 2011 (Pita et al. , 2013)) , was searched for in every selected cell. If no suitable habitat was found in a cell, the latter was no longer prospected and was excluded from the dataset.
We collected data on the type of the most representative riparian vegetation located within 2 m from the riverside (Veg_type) at each site: mesophilic or hygrophilic plants, woody vegetation. Vegetation height (Veg_h) was used to model both detection and occupancy of SWV. At each site, percentage of cover by aquatic vegetation (Veg_co) was visually estimated on the whole transect using four vegetation cover classes: 0, 1-25%, 26-50% and 51-100%. Vegetation cover might positively affect the capacity of SWV to move alongside the river. From aerial photos taken in 2012-2015, we estimated river width (River_w) on QGIS, from an average over 10 points located at equal intervals alongside the transect. As our intent was also to determine if the presence of SWV was affected by the presence of invasive species (muskrat and coypu), while accounting for habitat features, we assessed the use of a particular site by the invasive rodents (D_Onda; D_Myo) on a scale ranging from 0 to 3, based on the number of detections after three occasions.
This covariate was also altered into presence-absence data (species-specific presence: P/ A_Onda; P/A_Myo; presence of at least one invasive rodent: P/A_inv). As a second step, we assessed whether spatial segregation in habitat use between the three species occurs at small spatial scale (SWV home range). To achieve this, we selected, among the 98 sites surveyed for the large-scale protocol those with both SWV and at least one of the two invasive species detected during at least two occasions (N = 27). Each original transect was lengthened to 200 m and subdivided into 10 segments of 20 m long that constituted the adjacent spatial replicates. Each segment was sampled only once (between 15 June and 30 June) for 10 minutes, by the same observer to detect faeces of SWV, muskrat and coypu. Presence of muskrat (P/A _Onda ), presence of coypu (P/A _Myo ), presence of any of these two species: 0 or 1 (P/A _inv ) and species richness of invasive species: 0, 1 or 2 (Rich _inv ), were treated as categorical variables.
In each segment, we also characterised the herbaceous plant type (mesophilic or hygrophilic) and the height (≥30 cm) of riparian vegetation located between 0 and 0. 5 m (Veg_type1; Veg_h1) and 0. 5-2 m (Veg_type2_H; Veg_h2) from the riverside and presence of wood between 0. 5-2 m (Veg_type2_W). Local scale surveys were always carried out in periods without precipitation over the last three days. Only 18 sites of the 27 original ones were surveyed because of accessibility issues (reluctant landowners, development of dense thorns). The same observer visited every segment in June 2016. Two of these 18 sites were excluded following the important deterioration of the riverside vegetation due to intensive browsing by cows; the final dataset included 16 sites.
Statistical procedures For analysing the large-scale survey, we used hierarchical occupancy models to estimate SWV occupancy probability in relation to habitat features and occurrence of invasive species  (MacKenzie et al. 2002). We modelled the probability of detection p and occupancy rate ψ using single-season occupancy models with Program PRESENCE v. 11. 0  (Hines 2010). Model assumptions were that (1) occupied sites remain occupied for the duration of the survey, (2) the species is not detected when absent (i. e. no false positive), and (3) detection at one site is independent of detection at all other sites  (MacKenzie et al. 2002). The probability of detecting the invasive species at least once over three surveys was very close to 1 (see results), such that a multispecies occupancy modelling approach would have brought only little added value but too many parameters given the sample size available.
Introduction Depuis plusieurs décennies, on constate un fort déclin des réseaux hydrométriques dans le monde et en particulier en Afrique. Le Congo, plus grand fleuve d'Afrique, n'échappe pas à ce constat. Mais plusieurs innovations ou évolutions permettent de surmonter ces manques : l'OMM a par exemple développé HydroHub  (Dixon et al. , 2020)  ou encore WHOS (WMO Hydrological Observing System -Pecora et Lins, 2020), et des approches multidisciplinaires sont promues  (Tauro et al. , 2018). L'altimétrie spatiale, qui consiste à mesurer les hauteurs d'eau par satellite est une de ces innovations majeures. Sept entités françaises se sont ainsi regroupées au sein d'un groupe de travail pour valoriser l'altimétrie spatiale dans le bassin du Congo et proposer des applications opérationnelles. Après avoir rappelé le contexte, le présent article présente le projet d'appui du groupe de travail sur le bassin du Congo ainsi que les principaux résultats obtenus : augmentation de la base de données de stations virtuelles sur le Congo, étude comparative des données spatiales et in situ au droit d'un même site, transformation des altitudes en débits et utilisation pour des applications opérationnelles (navigation fluviale et hydroélectricité).
Published by Copernicus Publications on behalf of the International Association of Hydrological Sciences. Les mesures de hauteur d'eau par altimétrie sont accessibles sur le site Hydroweb (http://hydroweb. theia-land. fr, la date du dernier accès : 22 octobre 2021). Hydroweb est une base de données de variations des niveaux d'eau sur les lacs et les fleuves du monde à partir de l'altimétrie satellitaire, qui a été progressivement mise en place depuis 2003  (Crétaux et al. , 2011). Aujourd'hui, ce sont plus de 11 000 stations virtuelles qui sont disponibles partout dans le monde (Fig. 1 ). L'altimétrie spatiale permet ainsi de compléter le réseau de mesure in situ afin de densifier les données et d'assurer un meilleur suivi hydrologique  (Calmant et Seyler, 2006 ; Calmant et al. , 2008).
Objectifs et activités du projet Le groupe de travail a choisi le bassin du Congo comme bassin pilote avec les objectifs spécifiques suivants : (1) l'établissement d'une base de données de stations virtuelles, (2) l'étude de la conversion des élévations altimétriques en débits, (3) l'élaboration d'un système d'information hydrologique et (4) les applications sur l'hydroélectricité et la navigation.
Etablissement d'une base de données de stations virtuelles Le projet a permis de mettre en oeuvre près de 550 stations virtuelles sur l'ensemble du bassin du Congo (CICOS, 2016b, c). La calibration d'une station virtuelle a été réalisée avec une station in situ (Maluku-Trechot) installée au droit d'une trace de satellite. La précision estimée de la mesure altimétrique est ainsi de 14 cm (Fig. 2 ). Cette précision sera encore accrue avec SWOT, dont les incertitudes seront moindres.
lancé fin 2022 par le  Centre National d'Etudes Spatiales (CNESl'Agence Française de Développement (AFD)  et facilité par l' Office International de l'Eau (OiEau)  d'une base de données spatiales par l' Institut de Recherche pour le Développement (IRD) , a été proposée par l'Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE) pour passer des altitudes aux débits aux stations virtuelles. Des applications spécifiques ont par ailleurs été développées par la  Compagnie Nationale du Rhône (CNR)  pour l'hydroélectricité et la navigation fluviale.
AcknowledgmentsThis research has been supported by the German Federal Ministry for Economic Affairs and Energy (BMWi) under grant no. 50EE1721C. Authors also acknowledge support through ACTRIS-2 under grant agreement no. 654109 from the European Union's Horizon 2020 research and innovation program, and through PoLiCyTa from the German Federal Ministry of Education and Research (BMBF) under grant no. 01LK1603A. We also appreciate very much the fruitful discussions within the EVAA consortium (LMU, DWD, DLR), with the "Aeolus DISC" team, and the Aeolus Team of ESA. The processor development, improvement and product re-processing preparation are performed by the Aeolus DISC (Data, Innovation and Science Cluster), which involves DLR, DoRIT, ECMWF, KNMI, CNRS, S&T, ABB, and Serco, in close cooperation with the Aeolus PDGS (Payload Data Ground Segment).
Data Availability Statement The ground-based Polly lidar data is visualized at polly. tropos. de and will become publicly available via the data portal of the European Aerosols, Clouds and Trace gases Research Infrastructure (ACTRIS) when its implementation phase is completed. Aeolus aerosol spin-off data used in this publication is not yet publicly available, but, after reprocessing in spring 2021, including improved radiometric calibration and quality flags, an updated data set will become freely available via ESA services (i. e. , https://aeolus-ds. eo. esa. int/oads/access/ and the online VirES tool at https://aeolus. services/). Nevertheless, to allow the FAIR data principles, all data used for this study are available at zenodo. org under https://doi. org/10. 5281/ zenodo. 1115995.
Assessing the composition of the track-making groups Stature estimates, based on the strong correlation between stature and total footprint length quantified experimentally, were performed in two steps  (Duveau et al. , 2019). First, an average foot length to stature ratio (14. 8%) was determined from the experimental sample and bibliographic data (38 population samples; 37,328 individuals) including individuals of different geographical origins and behaviour (e. g. unshod and shod populations). This ratio was then applied to the experimental ratio between the footprint total length and the foot length (103. 7%) in order to obtain a linear relationship between the total length of the footprints (Ltot) and the stature (S): S = 6. 54 Ltot. A stature was also estimated for the footprints whose state of preservation did not allow measurement of the total length, by using the strong correlations of this length with the tarsometatarsal length (L tmt / L tot = 0.
82; r² = 0. 89) or, when it could not be measured, with the footprint width (w / Ltot = 0. 43; r² = 0. 66). The stature estimates were then used to determine an age class for each footprint. To do this, the estimated statures were positioned on a curve representing the variation between stature and age defined from published estimates of Neandertal osteological remains (figure  5 ;  Duveau et al. , 2019). This curve takes into account the variation of stature with age in children and assumes a constant stature in adults, equivalent to the average stature of all adults in the database (162. 5 cm;  Duveau et al. 2019). Given the small number of Neandertal adolescents and the difficulty of estimating a precise age for them, no individuals were used for this age class. The corresponding part of the curve was extrapolated to obtain a logarithmic variation.
Acknowledgements I would first like to express my gratitude to the  Société d'Anthropologie de Paris  that did me the honour of awarding me its 2020 Ph. D. prize. I am grateful to the two anonymous reviewers for their helpful comments. I would also like to thank my thesis directors:  G. Berillon ,  C. Verna  and  D. Cliquet. I wish to commend all the volunteers who participated in the excavations and experiments at  Le Rozel , making the discovery of the ichnological material and its analyses possible. I am also grateful to  L. Albessard-Ball  and  I. Bossanyi  for editing the English. The field work is supported by the  French Ministry of Culture  and the  French département of La Manche  and is directed by  D. Cliquet , who gave me the opportunity to study the footprints during my Ph. D.
thesis. The experiments were funded by the  CNRS -Institut Écologie et Environnement International Research Network IRN-GDRI0870  and the metric analyses were conducted under the  ANR-18-CE27-0010-01 HoBiS  programme. Finally, this study would not have been possible without the funding granted by the  École Doctorale 227 MNHN-SU  " Sciences de la nature et de l'Homme : évolution et écologie ".
Remerciements Nous remercions : pour leur assistance et l'accès à leur matériel,  Juliette Galpin  (MHNH),  Michel Martin  (MHNB),  Christian A. Meyer  (NMS),  Jean Michel Rose  et  Alain Vadet  (MHNB) ; le réviseur Ch. Püntener et le réviseur anonyme pour les importantes améliorations apportées au texte grâce à eux ; à Paris (MNHN. F) pour leur aide technique au dégagement :  Philippe Richir  et  Saloua Gmira , pour la bibliographie :  Marie-Astrid Angel , pour l'illustration :  Denis Serrette  (photographies) et Franc oise Pilard (dessins).
THE ARCHAEOLOGICAL SITES INCLUDED IN THE STUDY Natsargora: located in the Kashuri district in Shida Kartli, 7 km from the Kura river, at 770 m asl. Excavations were first conducted by a Georgian team between 1984 and 1992 and later by a Georgian-Italian team in  2011 -2012 (Puturidze and Rova 2012; Rova et al. 2017). The site consists of a small mound and the most recent excavations suggest a KA settlement that was abandoned and then a re-occupation of the site during the Early Kurgan period. Based on archaeological data and radiocarbon dates, the settlement is dated to the KA II (Rova 2014). Although 26 Early Bronze Age pit burials were excavated by the Georgian expedition (Puturidze et al. 2012), bone samples were not available for any of them. In contrast, a sampling of 10 animal bone remains from the KA settlement was done thanks to the 2011 Georgian-Italian excavation  (Rova et al.