dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Firstly, I would like to express my deep gratitude to the entire team that contributed to the development of the GRAVITY instrument. Without their tireless efforts and dedication, this project would not have been possible. Secondly, I extend my sincere appreciation to the members of the  ExoGRAVITY team , whose contributions were instrumental in enabling us to make these groundbreaking exoplanetary observations. Additionally, I would like to thank  Anthony Boccaletti  and  Aglae Kellerer  for their invaluable assistance in proofreading this paper. Lastly, I am grateful for the support of the  French ANR , which provided funding for this project through the grant Project -ANR-21-CE31-0017.
Acknowledgements I. C. warmly congratulates  Jean  for the  CNRS  Gold Medal and wishes express him all his gratitude for more than two decades of collaboration and friendship. I still remember when, as an undergraduate student, I got fond of Jean's articles on laser cooling and I was looking forward to meeting him in person. So, I could not miss the chance of spending a semester working in his group as a visiting student. This period, together with the following Post-Doc, were a real turning point of my own career, where I could give a dramatic boost to my scientific expertise and lay the grounds of many research lines that I am still now carrying out: the choice of the research project that he and  Yvan  proposed for my stay in Paris could not have been more far-sighted! Since then, I have always counted on  Jean  as a source of scientific inspiration and a continuous generous support for my research activity.
Funding. A. B. aknowledges financial support from Q@TN, the  joint lab between University of Trento ,  FBK-Fondazione Bruno Kessler ,  INFN-National Institute for Nuclear Physics and CNR-National Research Council. I. C. acknowledges financial support from the  European Union   H2020-FETFLAG-2018-2020  project " PhoQuS " (n. 820392 ), from the  Provincia Autonoma di Trento , and from the  Q@TN initiative.
The planets in orbit close to their stars are subject to different interactions with their environment, which we illustrate in the three panels of Figure  1. Note that in all panels, the horizontal axis represents the distance of the planet from the star which increases from left to right. Interest with detected candidate exoplanet (the horizontal axis corresponds to the planet orbital period) and detected stellar rotation period (vertical axis). The symbol size is scaled with the radius of the planet candidate, and the color labels the effective temperature of the host star. Adapted from  [6]. C. Distribution of exoplanets from the exoplanet. eu database (as of May 25 th , 2022), shown as a function of the mass of the host star (vertical axis) and the semi-major axis of the planet in units of the stellar radius (horizontal axis).
Acknowledgements A. S. thanks warmly  P. Zarka  for a careful read of this review and valuable suggestions. A. S. acknowledges funding from the  European Union 's  Horizon-2020 research and innovation programme  (grant agreement no. 776403 ExoplANETS-A ), the  PLATO/CNES  grant at  CEA/IRFU/DAp , and the  Programme National de Planétologie (PNP).
The spectral characterization of exoplanetary atmospheres is a relatively new subject. Until recently, space and ground-based observatories allowed only a few planets to be observed with spectroscopic instruments. The arrival of JWST data and the development of new instruments on Earth will drastically modify the situation, giving access to deeper modelling contraints and to a better understanding on planet formation and evolution. This paper is devoted to describe the turning point experienced by the astronomy of exoplanets that will continue in particular with the launch of the ESA/Ariel mission at the end of the decade. Résumé. Cet article passe en revue les récentes avancées en physique des atmosphères des exoplanètes, dans le cadre de la préparation de la mission Ariel de l'ESA. La modélisation des atmosphères, bâtie sur les modèles planétologiques développés dans l'étude du Système Solaire, peut aujourd'hui être extrapolée aux exoplanètes par l'utilisation de bases de données moléculaires adaptées aux températures rencontrées.
Variability and late type stars The exquisite resolution of SPHERE allows to obtain consistent measurements at several epochs to perform dynamical studies in debris disks. SPHERE has revealed an unexpected phenomenon in the one of a kind disk around the M-type star AU Mic which is young and ideally located at about 10 pc. Large structures forming some arches a few AU wide were observed during SPHERE commissioning in 2014 on one side of this edge-on debris disk  [117]. Not only the structures were all recovered in older data from HST (2010, 2011) but they featured an apparent motion, the interpretation of which is complicated by the edge-on orientation. Follow up observations in the next years confirmed the evidence of the motion, and discovered other smaller structures in different places of the disk, all being moving over time  [118]  (Fig.
In practice, we can derive up to three different 3 mass-distance relations for the host star  [52] : -The angular size of the source star being known, when it transits over a caustic it gives a measure of the angular Einstein ring radius Θ E , hence it gives a mass-distance relation for the host star (see Eq. (  1 )). -A second mass-distance relation is obtained when parallax due to the change of the alignment between observer source-lens during the event is measured from the microlensing light curve. Using ground-based data alone, the parallax is often not well constrained and there could be a degeneracy with the lens orbital motion. However, simultaneous ground and Spitzer observations (done in 2014-2019) can give strong parallax constraints when the source is bright enough  [17, 25, 53, 54].
Acknowledgements This article is based on the work done with my long term friends and collaborators,  David Bennett ,  Jean-Baptiste Marquette ,  Virginie Batista ,  Aparna Bhattacharya ,  Josh Blackman ,  Etienne Bachelet ,  Arnaud Cassan ,  Christian Coutures ,  Andrew Cole ,  Jadzia Donatowicz ,  Kym Hill , the late  John Greenhill ,  Naoki Koshimoto ,  Clément Ranc ,  Christophe Alard ,  Natalia Rektsini ,  Takahiro Sumi ,  Sean Terry  and  Katie Vandourou.
Funding. The author was supported by the  ANR   COLD-WORLDS  ( ANR-18-CE31-0002 ), the  Australian Government  through the  Australian Research Council Discovery Program  (project No. 200101909 ) and the  University of Tasmania  through the  UTAS Foundation  and the endowed  Warren Chair in Astronomy. Note. Follows up on a conference-debate of the French Academy of Sciences entitled "Exoplanets: the new challenges" held on 18 May 2021, visible via https://www. academie-sciences. fr/fr/Colloques-conferences-et-debats/exoplanetes. html. Note. Fait suite à une conférence-débat de l'Académie des sciences intitulée "Exoplanètes : les nouveaux défis" tenue le 18 mai 2021, visible via https://www. academie-sciences. fr/fr/Colloques-conferences-et-debats/exoplanetes. html.
Data We selected a large sample of catchments, from several international and national databases: the Global Streamflow Indices and Metadata Archive (GSIM)  [Do et al. , 2018]  consisting of a collection of streamflow time series for more than 35,000 catchments worldwide, the 9322 GAGE-II US stream gauges maintained by the U. S. Geological Survey (USGS)  [Falcone, 2011] , the HydroPortail database (http://www. hydro. eaufrance. fr), where flow measurements are available for more than 4000 stations across France  [Leleu et al. , 2014]  and the UK National River Flow Archive for which streamflow data for more than 1500 stations are available. Since the GSIM database contains catchments from the national databases (France, UK, US) we consider extracting data from these national databases preferable and excluding catchments for these countries from the GSIM database, to avoid any duplicated catchments.
The selection was made following two criteria: (1) Availability of at least five years of annual streamflow data between 1992 and 2015. This criterion stems from the use of plurennial water balance models and the availability of land use historical data. (2) Catchment area between 10 km 2 and 10,000 km 2. The motivation for the lower limit was due to the spatial resolution of the climate reanalysis data (1/24°), and the upper limit was set so that reduce mixing effects of both land use and climate over a given catchment. Applying the above criteria led to a selection of 6002 catchments. As many of these catchments are nested within each other, this poses the problem of redundant information that may bias model evaluation using classical cross-validation experiments. To remove redundancy, we subset the database to reach a final set of 5026 non-nested catchments (Figure  1 ).
The catchment represents a large variety of climatic conditions but with a lack of representation of drylands, originating from a lack of river network in these areas. The mean number of available years over the catchment set is 18 and the mean catchment area is 1800 km 2. According to the Koppen classification, majority of catchments (64%) are under mild temperate climate, 19% are under snow environment, 12% are under tropical climate and only 3% and 2% are under dry and polar climates respectively. Climatic data were extracted from the TerraClimate gridded data  [Abatzoglou et al. , 2018] , with a spatial resolution of 1/24°. Annual precipitation (P), Penman-Monteith potential evaporation (PET ), and net radiation (Rn) were retrieved for each catchment over the period 1992-2018 and temporally aggregated at an annual time scale.
TerraClimate uses climatically aided interpolation, combining highspatial-resolution climatological normals from the WorldClim dataset, with monthly data from other sources namely CRU Ts4. 0 and JRA-55. Land use data were extracted from the ESA CCI Land Cover time-series  [ESA, 2017] , for which historical land use maps are available at the annual timescale over the period 1992-2018 at a spatial resolution of 300 m. This dataset was produced in two steps: first, the Medium Resolution Imaging Spectrometer (MERIS) archive from 2003 to 2012 was used to construct a 10-year baseline land cover map, second, the annual time series are obtained by combining the baseline map with land cover changes detected from (i) Advanced Very-High-Resolution Radiometer (AVHRR) time series from 1992 to 1999, (ii) SPOT-Vegetation (SPOT-VGT) time series from 1998 to 2012 and (iii) PROBA-Vegetation (PROBA-V) and Sentinel-3 OLCI (S3 OLCI) time series from 2013 to 2019.
Catchments where wetlands are dominant, are poorly modeled but these catchments are very few (n = 10). For all models, the RMSE obtained in validation are quite similar to those obtained in calibration (Figure  7 ), showing that the models are spatially robust, i. e. , the spatial transferability of the model parameters is satisfying. This is reassuring and confirms that the dataset is broad enough to draw stable and   The parameter values obtained by calibrating the Tixeront-Fu model equation with 6 different land use classes are rather spread over the range of possible values, from close to unity to 5 (Figure  8 ), and relatively consistent with a priori expectations. The "Settlement" land use class reaches a low median parameter value of 1. 78 (interquartile range: 1. 72-1. 83) indicating low evaporation ratios, while flooded vegetation gets a median parameter value of 5.
Comparison with previous model parametrization and performance Before comparing the modeling results of this study with previous attempts, it is important to keep in mind that such comparison is complex due to (i) the relatively small number of large sample studies, (ii) the different target variables within these studies, and (iii) the different data prefiltering methods. We found that 449 catchments (9% of the total catchment sample) did not fall within the theoretical range for the evaporative ratio. This ratio is similar to previous studies on the global scale:  Peel et al. [2010]  and  Zhou et al. [2015]  found ratios of 19% and 10% for global streamflow datasets constituted by 861 and 1928 worldwide catchments, respectively. The choice of including these catchments or not for developing and assessing a hydrological model is not trivial.
On the one hand, including these catchments may lead to greater uncertainties in the estimations of model parameters value but on the other hand, excluding these catchments poses the question of the validity of the remaining catchments, that are still susceptible to have water balance issues. For these reasons, some authors included further data checks, including assessments of the impacts of dams or preliminary estimation of interbasin groundwater flows [de Lavenne and Andreassian, 2018], which may affect significantly water balance for some geological settings. This choice also has important consequences on the performance of the model since water balance formulations from the Budyko framework are not able to reproduce the behaviors of the catchment that do not fall within the theoretical range.
In 1994, de Marsily pointed out that the constraints of Greek tragedy (uniqueness of place, time, and action) applied to empirical hydrological models. Even with the progress in model developments and availability of worldwide hydro-climatic data, we shall acknowledge that de Marsily's view is still relevant. This study utilized data from several sources. For streamflow data and catchment delineation, we used GSIM database (https://doi. pangaea. de/10. 1594/PANGAEA. 887477) that makes available data from a large number of worldwide catchments. For the U. S. catchments, streamflow data were collected from the USGS website (available at http: //waterdata. usgs. gov/). For the French catchments, flow measurements were extracted from HydroPortail (available at https://www. hydro. eaufrance. fr/).
For the UK catchments, streamflow and contours data were collected from the National River Flow Archive (https://nrfa. ceh. ac. uk/data/). Precipitation and potential evaporation data were obtained from the TerraClimate database (available at https://www. climatologylab. org/terraclimate. html). We thank Antoine Carlier for his help on Figure  3 , the invited editors to provide us the opportunity to contribute to this special issue, Thibault Mathevet and the anonymous reviewer for their relevant suggestions.
Thus, for the period 2005-2012, we have at our disposal both the water level gauges and the data from the differential pressure sensor installed at the monitoring station. The long manual digitisation work, by creating the abscissa and ordinate axes from the reference data collected on-site, was carried out with the help of the software available online "PlotDigitizer". This software allows the digitisation of the graphical documents with an average resolution of 2 to 3 h directly linked to the speed of the water level gauge clock. Thus, the periods of rising floods, in particular, are correctly estimated. In a second step, a linear interpolation was carried out to produce a continuous series of water levels over the period 1972-2008. The 2005-2008 period has been considered as a validation period for comparison of digitised data versus the differential pressure sensor data.
For the period 2008-2022, we used the data available from the digitisation and the differential pressure sensor measurements were compared to validate the digitisation work. In general, the correspondence between the two sets of data was less than 3 cm. Some corrections related to the drift of the probe were also made. During the last fifty years, the hydraulic control of the rating curve at the outlet of the system has been very stable. Effectively, we estimate the discharge rate using only three successive rating curves of the station, including the construction of the artificial weir in 1976, illustrated by the step visible on the water level record. Therefore, the rating curve is still valid In conclusion, we already have at our disposal the daily discharge estimates from 1969 for the Baget system.
Data section Mammals' trait data: We gathered extant terrestrial mammalian herbivore (TMH) species traits data from available datasets: Phylacine (n = 5831), Mammal Diet 2 (n = 6625), Elton Traits (n = 5400), and PanTHERIA (n = 5416)  25, [29] [30] [31]. These datasets do not include semi-domesticated or range species, which were not part of our study. We harmonized taxonomic differences between data sets by using the Phylacine standardized synonym table. These datasets include life-history traits (body mass, diet preferences, longevity, etc. ) for all known mammal species. We complemented and verified these data using additional sources from peer-reviewed literature for two key traits in our analysis: digestive system (hindgut or foregut) and folivory type (browser, grazer, mixed-feeder).
Empirical mammal population density & biomass: We compiled a dataset of mammal density estimates from two main sources  32, 33  that were complemented with 433 records for a total of 7713 records. The complete list of density data can be found in the appendix. This dataset included 440 species, which represent roughly 50%, 15%, and 7% of the species that we later define as, respectively, large (n = 238), small (n = 108), and micro (n = 94). The mammal density data were collected between 1950-2020 in locations such as national parks or protected areas. The data had geographic and taxonomic gaps as often not all species present in each location were censused. Small and micro mammals had limited geographic coverage and were particularly underrepresented in the data set (Fig. S5 ). This lack of taxonomic coverage reduced considerably the estimates of total animal biomass per km² (Fig.
S5 ) and prevented the creation of complete HFT maps (discussed in the following section). We thus generated a complementary 2° resolution gridded dataset for all TMH species using two methodologies: 1) a spatial linear mixed-effects model (LMEM) following  20  and 2) allometric-scaling equations  34. The LMEM was built using the following predictors: body mass, grass and tree NPP from MODIS  35  , precipitation of the warmest quarter from WorldClim2  36  , and random effects to account for taxonomy and spatial pseudo-replication. In method 2) we used a set of allometric equation to determine density from body mass with slope and intercept that were family and order specific  34. Even though both methods present some shortcomings, LMEMs capture some of the influence of bioclimatic factors on mammal density  20.
We averaged the empirical data across multiple censuses and occurrences of the same species within each grid-cell. We then combined the LMEM gridded data with the empirical data only where a species was present according to its distribution map but absent in the empirical data. Using the empirical data-LMEM (or allometry) product we derived biomass per km² by multiplying density by 80% of the body mass reported in Phylacine to account for sexual dimorphism  37. Climate data, megafauna extinction maps, and analysis: We downloaded monthly plant actual evapotranspiration (AET) data from the TerraClimate dataset  38  and calculated yearly average over the period 1958 and 1980. None of the data after 1980 were chosen because we avoided years with elevated CO2 concentrations. We also wanted to approach as much as possible the early 20th century climate data used to model mammalian biomass (see modeling section).
Global classification of mammalian herbivores We used Phylacine as our taxonomic and diet reference database, as it is the most recent database and uses phylogeny to better estimate species diet composition  25. We selected all terrestrial species whose diet is composed of at least 80% plant material (n=2947) from which we excluded nectar feeders (≥ 20% of nectar in diet, n=78, average body mass 0. 190 kg). Nectar feeders were excluded because nectar is not simulated in vegetation models and thus their food availability could not be calculated (see next paragraph for more details). Species with a low percentage of nectar in diet (< 20%) were instead retained. We also excluded species classified in Phylacine as extinct (extinct in prehistory or after 1500 CA, n= 267, average body mass 613 Kg).
As a last resource, we used the genus average. We then assigned to each species a digestive system (hindgut or foregut) and a folivory type (grazer, browser, mixed-feeder). We referred to a published dataset to determine digestive system  41  , if we did not find a match at the species level, we used the genus and finally the family. We only used genus or family when all species within the taxonomic rank had the same digestive system, otherwise we referred to primary literature (complete list in supplementary material). We determined folivory type starting from species identified previously  42  , which we updated with articles published after 2013 (complete list in supplementary material). All other species' folivory type were assigned based on primary literature or following the genus to family strategy used to determine digestive system.
Each PFT is defined by a set of parameters related to photosynthesis, allocation strategies, and physical traits (most notably leaf traits). PFTs compete for space, water, and light and coexist in the same grid cell by occupying a prescribed fraction of the cell. ORCHIDEE keeps track of a large number of biomass and carbon pools above and below ground. Processes are simulated at various time steps from hourly to daily. After an initial spin up simulation to reach equilibrium in the carbon pools, a 300-year time series was generated by cycling through climate forcings from the years 1900-1950. The simulation included 13 PFT distributed according to a reconstructed land cover of ~1500 YBP. Crops were excluded because we simulated conditions in natural settings. The land cover map was created by merging the ESA CCI dataset (https://www.
esalandcover-cci. org/) and the LUH2 dataset (http://luh. umd. edu/). The ORCHIDEE simulation had a spatial resolution of 2° and was forced with CRU-NCEP six-hourly climate data  58  , reinterpolated to the 30-minute time step used by ORCHIDEE. In the time series used as REMAP input we made a distinction between the availability of browse and grass biomass: 100% of grass-leaf biomass was available for consumption instead only 20% of tree-leaf biomass could be consumed. In forest ecosystems only a fraction of leaf biomass is available or preferred for consumption. Only the understory leaf biomass is available to terrestrial herbivores and arboreal folivores consume mostly fresh leaves, which are highly digestible and represent a small percentages tree-leaf biomass. The 20% estimate is based on reported leaf understory biomass  59- 61  and was assumed to include twigs, bark, and small shrubs, which are not explicitly simulated in ORCHIDEE.
In ORCHIDEE 10% of NPP is allocated to fruit but there is no distinction between fruit and seeds. We thus split fruit biomass in equal parts assigned to separate seed and fruit pools, although we acknowledge that conifers do not produce fruit and might invest more in seeds. Most litterfall studies do not distinguish fruit and seeds making it challenging to establish an appropriate ratio  62. The biomass pools for leaves (browse & grass), fruit, and seeds are a critical input as they determine the availability of food for HFTs. These pools are related to ORCHIDEE's NPP which is allocated to different plant material. ORCHIDEE's NPP has been validated before but has not yet been evaluated for its capacity to simulate leaf, fruit, and seed biomass. As no large-scale datasets of leaf biomass were available, we compared tree and grass NPP from ORCHIDEE against Moderate Resolution Imaging Spectroradiometer (MODIS) NPP  35.