dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors are particularly grateful to the more than 2000 patients that anonymously filled in the feed-back forms after having experienced a telemedical examination. Without their help and without the feed-back of fellow practitioners, much of our thinking would have been mere speculation. Special thanks to  Professor Sarah Marshall-Pescini  of the  Konrad Lorenz Institute of Ethology, Department of Interdisciplinary Life Sciences, University of Veterinary Medicine, Vienna  who greatly contributed to the revision and English editing of the work.
In theory, a 80 Mpixels sensor is necessary to collect all the information captured by the MO for a 500 nm wavelength. Yet, cameras with this amount of pixels are very expensive (> 20 kC), not USB connected and bulky. A costeffective 20 Mpixels (Basler acA5472-17uc, see Table  1 ) was chosen for its number of pixels/price ratio. Outside the incubator, the system includes two LED drivers (T-cube, Thorlabs) connected to an Arduino Uno board with BNC cables. The Arduino itself is connected to a computer using an USB cable which allow to remotely control the output power of both LEDs. Open source software for acquisition control. Preview mode and control of the acquisition parameters are performed using a custom written code in the freely available Python language. This allows to further reduce the cost of the systemand to ensure open access and possible duplication by any other research group.
This was also at stake when it deals with Open-Science and making sure our device could be duplicated by anyone. Briefly, the code allows to communicate both with the camera using the Basler pypylon library (10) and with the Arduino using the Firmata library (11). Acquisition parameters such as the frame rate, exposure time,file name, repository location and data output of the camera as well as LEDs output power can be controlled using a graphical user interface. Preview live modes and long-term time lapse acquisition are provided. Our code gives the choice to save the experimental data in two different formats, tiff or hdf5 file (12). The latter is particularly interesting when dealing with large amount of data as its structure allows to open only subsets of a file. In practice, low-resolution images of a time lapse can be opened quickly to check the status of the sample while the experiment is still on.
The code is publicly available in our GitHub repository (9). Characterization of the optical performances. The resolution and the size of the FOV of the system were first investigated using a USAF 1951 resolution test target in the bright-field mode. First, from the results displayed in figure  2 , we estimate the FOV to be equal to 5. 5 × 8 mm 2. Second, the smallest resolvable line is the element 3 of the group 7. This corresponds to a spatial resolution of roughly 3. 1 µm. Given the magnification of the system and the pixel size, the theoretical resolution of the Incubascope is 3. 2 µm. Thus, the experimental value is in agreement with the theoretical one. From the same experiment, the collection efficiency across the FOV is investigated. From the background intensity plot-.
This is the reason why, even though our system has a large DoF, an egg can be in focus at a given frame but slightly blurred at the next one. Then, we captured images at the highest frame rate possible with our system. In theory the camera can acquire images up to 17 frames per second (fps), but this value is reduced in practice to 10 fps with our acquisition software. These performances are however sufficient to precisely capture the displacements of mature A. salina across the basin. Figure  3. C displays four consecutive frames showing a collision between two specimens swimming in opposite directions. More data are provided in the supplementary information.
The cost of the entire system is approximately 5500 C and includes the optics, the electronics, the light sources and the mechanical parts, but excludes the price of the incubator. The code used to control and perform acquisitions is written in the open source Python language, and thus does not require to have access to a specialized software licence (e. g Matlab or Labview). Light source components and associated drivers cost around 1000 C. To further reduce the cost of the system, we may envision to use unmounted LEDs such as Adafruit light sources, which are powerful, compact and easily controlled with an Arduino board. Regarding the optical elements, cost-effective solutions had been proposed to replace the MO by a single lens or even to use lens from cell phones. Yet these solutions usually diminish the optical performances and also require to adapt components or dismantle commercial products.
Funding Information This work was supported by the  Agence Nationale pour le Rcherche (MecaTiss , ANR-7-CE30-0007-03 ,  Parkington , ANR-17-CE18-0026-02 ,  MecanoAdipo ,  ANR-17-CE13-0012-01 ),  Cancéropôle GSO, SIRIC Brio (Bordeaux)  and the following charities:  Association pour la Recherche sur le Cancer (ARC) ,  La Ligue Contre le Cancer (Gironde and Pyrénées Atlantiques). A. Badon  thanks the  Fondation des Treilles  for its financial support.
We assess this in a network of in situ HS observations over the French Alps, French Pyrenees, and Andorra, with contrasted observation densities. We use CrocO, an ensemble data assimilation system of snow cover modeling  (Cluzet et al. , 2021a). CrocO is built around an ensemble version of the operational modeling system of Météo-France  (Vionnet et al. , 2012; Vernay et al. , 2021) , accounting for modeling uncertainties from the meteorological forcings  (Charrois et al. , 2016; Deschamps-Berger et al. , 2022)  and the snowpack model itself  (Lafaysse et al. , 2017; Dumont et al. , 2020). CrocO includes several versions of the PF tailored for the propagation of information from observed into unobserved areas  (Cluzet et al. , 2021a). These variants are used in a localized framework, in which only observations coming from a certain radius around the considered location are assimilated  (Van Leeuwen, 2009; Penny and Miyoshi, 2016; Poterjoy, 2016; Farchi and Bocquet, 2018).
Setup Assessing the ability of data assimilation to propagate information requires use-independent data for validation. We opted for a leave-one-out setup in which local observations are removed from the set of observations used in the local PF analysis. Only weekly observations were assimilated, while all available observations between 1 October and 30 June were kept for evaluation. There are two key design parameters for the data assimilation system: the value of the localization radius (large or small) and the choice of the PF algorithm (rlocal or klocal). Both exert a direct or indirect control on the number of observations simultaneously assimilated by the PF, and therefore, on its potential degeneracy and its ability to transfer information between locations. Experiments respectively combining the rlocal and klocal algorithm with four different localization radii were conducted: ranging from 17 km (the radius of an idealized circular SAFRAN massif of 1000 km 2 ) to 300 km (the maximal distance between two observations inside the Pyrenees and the Alps) with two intermediate radii of 35 and 50 km.
g. ,  Bellier et al. , 2017). Their flatness is a stronger condition for an ensemble's reliability than the SS = 1. The Continuous Ranked Probability Score (CRPS; (Eq. 7);  Matheson and Winkler, 1976 ) is an aggregate, ensemble score evaluating the reliability and resolution of an ensemble based on a verification dataset. An ensemble has a good resolution when it is able to issue different forecasts on different events (contrary to the climatology;  Atger, 1999). If we denote F p,t the ensemble Cumulative Distribution Function (CDF) and O p,t the corresponding observation CDF (Heaviside function centered on the truth value), the CRPS is com-puted at (p, t) following: CRPS p,t = R (F p,t (x) -O p,t (x)) 2 dx ∀(p, t) ∈ [1, N pts ] × [1, N t ]. (7) The CRPS skill score (CRPSS) is commonly used to compare the performance of an ensemble E to a reference R.
Acknowledgements. The authors would like to thank the reviewers for their comments which helped improve the paper,  Emmanuel Cosme  from the  Institut de Géosciences de l'Environnement in France  and  Tobias Jonas  from the  WSL-Institut für Schnee-und Lawinenforschung SLF  for helpful discussions and comments, and  Vincent Vionnet  from  Environment and Climate Change Canada  for suggestions on how to improve the quality of our figures. Financial support. Marie Dumont  was partially funded by  ANR JCJC EBONI. Marie Dumont  has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( IVORI  (grant no. 949516 )). CNRM/CEN is part of  Labex OSUG@2020. Review statement. This paper was edited by  Masashi Niwano  and reviewed by three anonymous referees.
Code availability. The Crocus snowpack model (including all physical options of the ESCROC system) and the particle filter algorithm are developed inside the opensource SURFEX project. The source files of SURFEX code are provided at https://doi. org/ 10. 5281/zenodo. 5111449 (Cluzet, 2021b)  to guarantee the permanent reproducibility of results. However, we recommend that potential future users and developers access the code from its Git repository. A documentation is provided at https://opensource. umr-cnrm. fr/projects/snowtools_git/wiki (CNRM, 2022). The Surfex tag used in this work is CrocO_v1. 1. However, as this software could not be applied outside the Météo-France HPC environment, CrocO Python software offers the possibility to run CrocO simulations locally. This functionality was not used here due to the high numerical cost of our simulations, which required the use of the Météo-France HPC environment.
Data availability. SAFRAN reanalyses, corresponding to the unperturbed forcings, are available at: http://doi. org/10. 25326/37# v2019 (need to select the postes domain) (Vernay, 2020). Additional input data necessary to reproduce the paper's simulations and figures are provided at http://doi. org/10. 5281/zenodo. 5115557 (Cluzet, 2021c). This archive includes: name lists, configuration files, spin-up files and pre/post-processing scripts. Simulation outputs represent a considerable amount of data (> 300Go) and are archived in Météo-France archiving system and can be accessed upon reasonable request to Matthieu Lafaysse (matthieu. lafaysse@meteo. fr).
While it has been shown several times that other tests of introgression can be deceived by ghost lineages  [21] [22] [23] , and even though the authors of the D 3 method call for cautiousness when interpreting the results of the test because of these ghosts, the scale of this problem for the D 3 test is still unknown. In practice, it is possible to estimate the probability that ghost introgressions lead to erroneous interpretations of the D 3 statistic. To this end, we simulated random species trees (40 extant species) and random introgression events with the ms software (see Material and methods), assuming that the age of the root of the trees was compatible with introgressions occurring uniformly between the species in the trees, but incompatible with introgressions with species outside.
Data underlying this figure can be found on Zenodo (doi: 10. 5281/zenodo. 6901799) and GitHub (https://github. com/theotricou/Ghost_branch_length/tree/main/1_D3). https://doi. org/10. 1371/journal. pbio. 3001776. g003 To test the effect of ghost lineages on the strategy proposed by Fontaine and colleagues  [13] , we simulated a species tree from which we chose 3 closely related species to mimic the 3 species of the original study that diverged only 1. 8 My ago  [13]  when the age of the Anopheles genera is dated between 25 and 93 My  [28] [29] [30]. Using ms, we simulated gene trees under 2 simple evolutionary scenarios (see Material and methods): one involving introgression between ingroup species B and C (scenario 1) and one involving introgression from a ghost lineage from outside the ingroup to species B (scenario 2,  Fig 4).
2 × 10 -16 ). Data underlying this figure can be found on Zenodo (doi: 10. 5281/zenodo. 6901799) and GitHub (https://github. com/ theotricou/Ghost_branch_length/tree/main/2_Anopheles). https://doi. org/10. 1371/journal. pbio. 3001776. g004 eukaryotic cell, supported a late acquisition of mitochondria. Precisions  [31]  and critics  [32]  about some of the theoretical, methodological, and statistical aspects of this study have already been formulated and commented on by the authors  [33]. Here, we focus on an overlooked one: ghost lineages. Their existence was mentioned earlier as a possible "caveat[s] accompanying conclusions coming from stem-length methods"  [31] , but their impact on the method was never evaluated. If ghost lineages are taken into account, the expectations of the stem-length method can be totally reversed.
Effect of the proportion of species sampled (A) and the length of the time interval between acquisition events (B) on the percentage of right (blue bars) and wrong (red bars) predictions of the order of events using the stem-length method. The dashed red line represents what would be observed if predictions were random. Cases where the stem-length method could not order events (because stems had the same length) were removed, which explains why the blue and red bars do not add up to 100%. Data underlying this figure can be found on Zenodo (doi: 10. 5281/ zenodo. 6901799) and GitHub (https://github. com/theotricou/Ghost_branch_length/tree/main/3_Stem-length). https://doi. org/10. 1371/journal. pbio. 3001776. g006 actually happened). This proportion reached almost 50% (the maximum possible, equivalent to a random prediction) when 1% of the species were sampled (Fig  6A ).
Of note, the use of these methods is often restricted to datasets for which the availability of archaic genomes can help validate the predictions made, such as primate (including humans  [45, 46] ) or bear  [47]. One direction of research could be the development of methods to detect ghost lineages or to disentangle ghost introgression from known lineage introgression. This would increase our knowledge of ghost lineages, which in turn may help to solve the interpretation issues we just demonstrated. However, we feel that it is not a necessity to "unghost" the lineages to handle them in the interpretations of current methods: Given their overwhelming importance, it is more promising to highlight the fact that most HGF-detection results are interpreted without considering ghost lineages and that failure to consider their impact on the interpretation of such tests impair their usefulness and lead to erroneous conclusions.
The effect of ghost lineages on the D 3 test for introgression Simulating species trees and introgressions. A total of 200 random species trees with a birth-death model were simulated using Zombi  [48]. Speciation and extinction rates were fixed at 1 and 0. 9, respectively. Simulations were run until 40 extant species were reached, then, 20 species were sampled. We converted the topology of the species tree into a suitable format for the coalescent simulator ms  [49]  with a custom python script: Branch lengths were converted into units of generation and the age of the root of the trees was fixed at 10 6 generations. To simulate introgression, a single migration event was imposed over 1 generation for a fraction f = 50% of the donor population targeting the recipient population. This migration rate was used to ensure that introgression detection using D 3 would not be biased by false positives.
Using branch lengths to determine the correct species tree topology Species tree simulation with ingroup or ghost introgression. A species tree was simulated using Zombi  [48]. Unless otherwise stated, parameters were the same as the one used in the previous section. The simulation was run until 16 extant species were reached. On this tree, 3 species with the topology ((A,B),C) were arbitrarily chosen. All others were considered to be ghost lineages. We then converted the topology of the tree into an ms readable tree for coalescent simulation. Two datasets were generated with ms; in both datasets, the number of generations separating the tip from the root of the tree was fixed to 5 × 10 6 and a migration event was imposed for a fraction f = 20%. For the first dataset, migration took place from B to C, i.
e. , between 2 extant species (ingroup introgression). For the second dataset, migration took place between a randomly sampled ghost lineage outside of the triplet phylogeny and B (ghost introgression). For both models, we simulated 1,000 gene trees evolving in populations of fixed size (Ne) of 100,000 individuals. Branch length in gene trees. We identified the gene trees that were congruent with the ((A,B),C) topology (corresponding to the branching order of the species tree) and those that corresponded to 1 of the 2 discordant topologies ((B,C),A) or ((A,C),B) that can arise from ILS or introgression. Subsequently, for both introgression models described above and for each gene tree, we computed the value of species divergence times T1 and T2 (see  Fig 4)  following the equation from Fontaine and colleagues  [13]  (see supporting information S3.
Bian et al. (2018)  recently developed the physically-based unified four-component (UFR97) model. Their radiative scheme is an extension of the two-component  Francois et al. 's (1997)  model and incorporates bi-directional aspects from  Yan et al. (2012). They tested the model on homogeneous, row-crop and forest covers (assuming a spherical foliage projection) where they showed that it could satisfactorily simulate directional temperatures with component sunlit/shaded soil/vegetation temperatures used as input. The relatively easy to implement UFR97 method can thus be used for direct assimilation of directional TIR data and thereby help to address directional anisotropic issues in surface energy balance inversion schemes. In this study, we present an evaluation of a coupled SPARSE-UFR97 model (hereafter SPARSE4) meant for inverting directionally anisotropic thermal data for evapotranspiration and water stress estimation.
Data description 2. 2. 1. Study sites The datasets used to run the models and for performance evaluations are drawn from four contrasting sites. Two Olive Orchards located in: Nasrallah, Tunisia (Latitude, Longitude: 35. 30 • N, 9. 92 • E: 2014) and Agdal, Morocco (31. 60 • N, 7. 98 • W: 2003) with vegetation cover fractions of ~7%  (Chebbi et al. , 2018)  and ~60%  (Er-Raki et al. , 2009) , respectively. Experimental datasets for the other two sites were collected during the growing periods of Soybean: 1990 (Avignon: 43. 90 • N, 4. 80 • E, France;  Olioso et al. , 1996)  and flood-irrigated wheat: 2004 (R3: 31. 67 • N, 7. 59 • W, Morocco;  Duchemin et al. , 2006) , hence varying vegetation cover fractions Table  1. provides a summary of the input data collected from the sites. These can broadly be categorized into: data used for model runsmeteorological, biophysical information; and evaluation dataflux measurements from the installed radiometers and eddy covariance systems.
Meteorological and surface biophysical input variables Forcing data collected from the meteorological stations at the four locations include air temperature, relative humidity, wind speed and direction. These are recorded at heights of 9. 2, 3, 9. 8 and 2 m for Agdal, Avignon, Nasrallah and R3 sites, respectively. Surface temperature, which is needed to force the surface energy balance, is also measured onsite using Apogee Infra-red radiometers (Apogee Instruments Inc. , UT, USA) observing from zenith. The R3 study site is also equipped with an oblique-viewing radiometer (at 45 • elevation). Surface temperature in Avignon is measured using a Heimann kT17 thermal radiometer. Additionally, incoming solar and sky radiation data from the installed pyranometers and pyrgeometers were available. See Appendix D for a summary of the instruments.
Except for Avignon, where recordings were made at hourly intervals, measurements at the other sites were collected on half-hourly basis. Other than angular data (i. e. , viewing azimuth, solar zenith and azimuth angles), no additional information is required to run the extended model for a homogeneous canopy that assumes a randomly inclined foliage. The solar zenith and azimuth angles can be calculated from the local time and geographic coordinates of an area of interest; the Sun angles and daylength algorithm  (Campbell & Norman, 1998; Iqbal, 1983 ) is used herein.
Data Source Range Characteristics (both model formulations)   Olioso et al. (1996) ; * c  Boulet et al. (2015) ,  Gentine et al. (2007). velocity components. The raw EC data at the Agdal site was processed using the 'ECpack' processing tool developed by the Meteorology and Air Quality Group, Wageningen University  (Hoedjes et al. , 2007). Nasrallah's EC system data was analysed using the 'eddy pro' software developed and maintained by LI-COR Biosciences and the 'ReddyProc' tool used for gap-filling  (Chebbi et al. , 2018). For R3, processing of the raw data was done using the 'EdiRe' software package from the University of Edinburgh  (Duchemin et al. , 2006). The ground heat flux was measured using soil heat plates installed within a few centimeters depth (a correction is applied to account for the heat storage between the sensor and the soil surface).
(2009) , i. e. 0. 44ET 0 + 0. 49; where ET 0 [mm d -1 ] is the daily reference evapotranspiration (estimated in their work using the FAO-56 Penman-Monteith equation). For Nasrallah, performance reporting primarily focuses on the dataset collected over the year 2014 whereas some other evaluation variables are drawn from years 2013 and 2015. This is because continuous meteorological, EC and sap-flow data that had minimal errors were readily available for year 2014 while other data were collected in the other years. For instance, measurement of shaded soil temperatures only began in year 2015; however, the turbulent flux as well as sap flow measurements collected during that year had a lot of intermittent but frequent instrument-related errors. In this isolated tree agro-system, positioning an infrared thermometer in the shade or over the sunlit soil was technically straightforward, while the sunlit and shaded leaf elements were more homogeneously distributed and could not fall within the field-of-view (FOV) of a single instrument.
Global fluxes and partitioning In this and subsequent sections, the 'prescribed' model runs were  forced with surface temperature measurements acquired at nadir, except for the R3 site which also had oblique TIR observations. Performance in estimating total fluxes is analyzed first, then we look at how those fluxes were partitioned between the vegetation and soil sources, and finally on the estimated evaporation efficiency. For an initial overview of how the models perform under different atmospheric conditions, outputs from clear skies were distinguished from cloudy days following the method detailed in section 2. 1. 2. However, throughout the rest of this study focus is mainly on outcomes from the combined clear-sky and overcast datasets. Further reporting on the nadir-and oblique-derived estimates at the R3 site is discussed in section 4.