dataesr/scientific-paragraphs-categorization huggingface dataset:
Group count indicators. Four studies published in 2015 introduced the concept of considering crop species produced and animal species reared by each farm in terms of groups, either based on common agronomic traits  [26, 27]  or based on similar nutrient composition  [13, 28]. Ten other studies using similar concepts were published afterwards. These simple and discrete indicators are always based on a sum of the number of crop and/or animal groups produced by each farm. Their use could be challenging considering the number of groups and the rationale for defining groups. For example, any indicator using the same groups as the MDD-W has to classify vegetables and fruits according to their vitamin A content (providing at least 60 retinol activity equivalents per 100 g  [29] ). Because these indicators overcome the issue of counting species with similar nutrient composition multiple times  [23] , they might be considered as more adapted when studying nutrition-sensitive agriculture, especially if they are based on the same groups used for the calculation of the MDD, WDDS, or MDD-W which are dietary diversity indicators that have been validated against multi-site quantitative food intake datasets as proxies of nutrient adequacy of the diet  [6].
Lilith is a public Python library for constraining new physics from Higgs signal strength measurements. Version 2. 0 of Lilith comes with an extensive XML database which includes the ATLAS and CMS Run 2 Higgs results for 36/fb, in addition the the Run 1 results. Both the code and the database were extended from the ordinary Gaussian approximation employed in Lilith-1. 1 to using variable Gaussian and Poisson likelihoods. Moreover, Lilith-2 can make use of correlation matrices of arbitrary dimension. We will report on these novelties and ongoing developments. The importance of how correlations and uncertainties are treated will be demonstrated by means of detailed validations of the implemented experimental results. Moreover, we show the effects for global fits of reduced Higgs couplings, 2HDMs of Type I and Type II, and invisible Higgs decays.
The Higgs data have therefore become a major guideline for BSM theories. Assessing the compatibility of a non-SM Higgs sector with the ATLAS and CMS results requires to construct a likelihood, which is a non-trivial task. While this is best done by the experimental collaborations themselves, having at least an approximate global likelihood is very useful, as it allows theorists to pursue in-depth studies of the implications for their models. For this reason, the public code Lilith  [1] [2] [3]  was created, making use of the Higgs signal strength measurements published by ATLAS and CMS, and the Tevatron experiments. Lilith is a lightweight Python library that uses as a primary input signal strength results ( , ) ≡ ( ) BR( → ) SM ( ) BR SM ( → ) , (1) in which the fundamental production and decay modes are unfolded from experimental categories.
Here, the main production mechanisms are: gluon fusion (ggH), vector-boson fusion (VBF), associated production with an electroweak gauge boson (WH and ZH, collectively denoted as VH) and associated production with top quarks, mainly ttH but also tH. The main decay modes accessible at the LHC are → , → * → 4ℓ and → * → 2ℓ2 (with ℓ ≡ , ), → ¯ , → + -and recently also → + -. Version 2 of Lilith  [3]  features a better treatment of asymmetric uncertainties through the use of variable Gaussian and Poisson likelihoods (as compared to the normal Gaussian approximation in version 1). Moreover, Lilith-2 can make use of correlation matrices of arbitrary dimension. The experimental results used are stored in an easily extendible XML database. The current official release comes with database DB 19. 09, which includes the full set of published ATLAS and CMS Run 2 Higgs results for 36 fb -1 as of September 2019.
Lilith 2. 0 release, database 19. 09 Likelihood approximations For a proper inclusion of the recent Run 2 results from ATLAS and CMS, several improvements were necessary in Lilith. First of all, the ordinary Gaussian approximation was no longer sufficient to model the likelihood. We have therefore extended the parametrisation of the likelihood to Gaussian functions of variable width ("variable Gaussian") as well as generalised Poisson functions, following the prescriptions of  [8]. Moreover, Lilith can now make use of correlation matrices of arbitrary dimension (for Gaussian likelihoods; Poisson likelihoods are still limited to 2 dimensions). We have also added the tH and the gluon-initiated ZH production modes, and corrected some minor bugs in the code. The exact way all the above is implemented is described in detail in  [3].
Experimental results in DB19. 09 The current official release, Lilith-2. , comes with database DB 19. 09, which includes the full set of published ATLAS and CMS Run 2 Higgs results for 36 fb -1 as of September 2019, summarised in Tables  1  and 2. Each result has been carefully validated. As an example, Figure  2  shows the validation for the CMS combination paper [10] by means of a vs. fit. Both versions of the implementation use the full 24 × 24 correlation matrix given in  [10]. However, in the version shown on the left, the plus and minus uncertainties for the 24 channels are described by ordinary Gaussian functions with symmetrised errors, while on the right they are described by Gaussians of variable width. As can be seen, there is a small difference in the resulting errors on and , but most importantly there is a significant shift in the best-fit values.
Clearly, the variable Gaussian agrees much better with the official CMS fit, and is hence used in the Lilith database. In some cases, reproducing the official coupling fits is more tricky. As an illustrative example, we show in Figure  3    using best-fit values and uncertainties for the signal strengths for each production (ggH, VBF, WH, ZH, ttH) and decay ( , , , , ¯ , ) mode combination together with the 24 × 24 correlation matrix from the CMS combination paper [10]. The 1 , 2 and 3 regions are shown as red, orange and yellow areas, respectively, and compared to the 1 and 2 contours from CMS (blue dots). On the left, the plus and minus uncertainties are described by ordinary Gaussian functions, while on the right they are described by Gaussians of variable width. official ATLAS coupling fit.
Ongoing developments and deployment Lilith is open source software distributed under the terms of the GNU General Public License. It is available from https://github. com/sabinekraml/Lilith-2/. The current official release, v2. 0 with DB19. 09 (see the releases page) is written in Python 2. A Python 3 version is available as v2. 1 pre-release. The usage of the code is explained in  [2]  and the tutorial session of this workshop. Ongoing developments concentrate on the extension of the database with the ATLAS and CMS results for full Run 2 luminosity. Some implementations+validations are straightforward, like for the CMS measurement in the → channel, CMS-PAS-HIG-19-010  [27] , shown in Figure  7  (left): here, the best-fit values and uncertainties for (VBF, ) and (ggH, ) are taken from Fig.
Conclusions We presented Lilith-2, a light and easy-to-use Python tool for constraining new physics from signal strength measurements of the 125 GeV Higgs boson. The main novelties of v2. 0 are: • a better treatment of asymmetric uncertainties through the use of variable Gaussian or Poisson likelihoods where appropriate; • the use of multi-dimensional correlations whenever available; • a well-validated database (DB 19. 09) including the published ATLAS and CMS Run 2 Higgs results for 36 fb -1. We discussed the modelling of the likelihood and the implementation and validation of the results in the database. Moreover, we showed examples for combined fits of reduced Higgs couplings, 2HDMs of Type I and Type II, and invisible Higgs decays. Lilith-2 is publicly available on GitHub and ready to be used to constrain a wide class of new physics scenarios.
It is also interfaced from micrOMEGAs [32] (v4. 3 or higher) and can thus easily be used for dark matter models-this is particularly useful for, e. g. , constraining Higgs decays into dark matter particles in a consistent manner. The code was recently translated from Python 2 to Python 3 in order to meet up-to-date standards. An extension of the database with the available ATLAS and CMS results for full Run 2 luminosity (about 140 fb -1 ) is in preparation. Given the high interest and ease of use of the signal strength framework-indeed a multitude of theory studies are based on Higgs signal strength results-we kindly ask the experimental collaborations to continue to provide detailed values for the various Higgs production×decay modes, including their correlations. Reference  [33]  lists detailed recommendations in this respect, to achieve optimal usefulness of the experimental results.
Despite the large number of species distribution modelling (SDM) applications driven by tracking data, individual information is most of the time neglected and traditional SDM approaches commonly focus on predicting the potential distribution at the species or population-level. By running classical SDMs (population approach) with mixed models including a random factor to account for the variability attributable to individual (individual approach), we propose an innovative five-steps framework to predict the potential and individual-level distributions of mobile species using GPS data collected from green turtles. Pseudo-absences were randomly generated following an environmentally-stratified procedure. A negative exponential dispersal kernel was incorporated into the individual model to account for spatial fidelity, while five environmental variables derived from high-resolution Lidar and hyperspectral data were used as predictors of the species distribution in generalized linear models.
Both approaches showed a strong predictive power (mean: AUC > 0. 93, CBI > 0. 88) and goodness-of-fit (0. 6 < adjusted R 2 < 0. 9), but differed geographically with favorable habitats restricted around the tagging locations for the individual approach whereas favorable habitats from the population approach were more widespread. Our innovative way to combine predictions from both approaches into a single map provides a unique scientific baseline to support conservation planning and management of many taxa. Our framework is easy to implement and brings new opportunities to exploit existing tracking dataset, while addressing key ecological questions such as inter-individual plasticity and social interactions.
-Sector B (Brisants, n = 5): located north of sector A and characterized by a fore reef and reef flat of medium width. -Sector C (Boucan, n = 6): located at the north of the study area and characterized by a narrow fore reef and reef flat. In-water turtles were captured by scuba diving. Once captured, the curved carapace length (CCL) was measured following Eckert et al. 's procedure (1999), and body mass was taken using an electronic dynamometer. Argos Fastloc-GPS tags (Wildlife Computers Redmond, WA, USA) that provide Fastloc-GPS data relayed via the Argos satellite system (<www. argos-system. org/>) were then fixed on each juvenile green turtle  (Chambault et al. 2020a for details). In order to increase the number of positions recorded, the tags were programmed to record GPS locations at a sampling interval set at 30 min.
Data pre-filtering Due to the restricted dispersal pattern commonly observed in juvenile green turtles in their coastal habitats and the large uncertainties associated with Argos locations, only Fastloc-GPS locations were retained for the analysis to improve the quality of the results and provide reliable kernel estimates  (Thomson et al. 2017). The Fastloc-GPS data were filtered to reduce measurement errors by removing locations with residuals values above 35 and locations recorded by less than five satellites  (Dujon et al. 2014). We restricted our dataset to positions associated with a travel speed lower than 5 km h -1  (Schofield et al. 2010). Finally, remaining positions located on land were discarded. The tracking data are summarized in the Supporting information. Chambault et al (2020a)  have shown a diel pattern in terms of habitat use between the diurnal reef flat habitat and the nocturnal slope habitat for green turtles of La Reunion.
Environmental variables Five physical variables were used to investigate the drivers of the turtles' coastal movements and predict their distribution. These variables were derived from high resolution Lidar (Light detection and ranging) and hyperspectral airborne data collected in 2009 and 2010, and processed over the west coast of La Reunion  (Mouquet et al. 2015a, Bajjouk et    2019). Processing of these data provided the following layers (Supporting information): 1) The bathymetry. The fine-scale gridded bathymetry (spatial resolution of 1 m, down to a maximum depth of 40 m) was provided by the SHOM (Service Hydrographique et Océanographique de la Marine, <https://data. shom. fr/ donnees>) acquired with a bathymetric Lidar sensor. 2) The slope. The maximum rate of change in elevation between a given point and its surrounding points (expressed in degrees) was calculated from Lidar in 3 × 3 m grid cell, and then the median was extracted in cells of 19 × 19 m.
5) The distance to rocks. This variable was used to identify potential feeding areas (e. g. benthic algae on hard bottom) and/or resting areas (e. g. caves). The hard (rocks and reef patches) versus soft substrates (sand and rubbles) were first discriminated based on simple thresholding of the image data 'Pseudo-colour images of the seabed of the reef areas of the west coast of Reunion Island'  (Mouquet et al. 2015a ) from the HYSCORES project  (Mouquet et al. 2015b). This data was extracted from hyperspectral images at a 40 cm resolution. The shortest distance between each grid cell and the closest identified rock pixel was then calculated in a grid of 1 × 1 m. All environmental predictors were then resampled to get the same spatial resolution of 5 × 5 m.
Species distribution modelling In order to model the different aspects of the geographical distribution of juvenile green turtles, we built two types of SDMs. The first SDM (called hereafter 'Population approach') reflecting the potential distribution of the entire population was fitted using generalized linear models (GLMs) applied to the occurrences' records of the 21 individuals and a pseudo-absences dataset. The second model (called hereafter 'Individual approach') inferred the realized distributions of each of the 21 individuals and reflects the portion of the potential niche space that is effectively occupied by individuals. Generalized linear mixed-models (GLMMs, from the lme4 package in R) including the individual as random factor were fitted by incorporating dispersal-related covariate (Dispersal coefficient) in the model in addition to the five environmental variables.
Dispersal-related variable calculation To take into account the spatial fidelity of each turtle in the Individual approach, a dispersal-related variable was included in the GLMMs. We used a negative exponential dispersal kernel  (Meentemeyer et al. 2008)  to quantify the degree of fidelity of each individual to each pixel of 5 m within the study area (hereafter called 'Dispersal coefficient': DC) -Fig. 2 , step 2 left. The parameter DC modifies the form of the dispersal kernel where low values of DC indicate localized fidelity in the vicinity of occupied sites, whereas high values indicate potential for dispersal from any occupied site. This DC was calculated using the iForce function from the iSDM package  (Hattab and Lenoir 2016). A logarithmic sequence of 10 DC values ranging between 0 and 1 was tested for each turtle to set the optimal DC value ('Individual approach via GLMMs' section for the DC choice).
Spatial modelling Population approach via GLMs A three-fold cross-validation was used by partitioning the dataset of each run into the training (2/3 of the data) and the validation dataset (1/3). Ten binomial GLMs (one model for each pseudo-absence dataset) were performed on the training dataset using the presence of turtles (1: presence versus 0: pseudo-absence) as a response variable, with a logistic link function (Fig. 2 , step 3 right). The ten GLMs included five predictors: slope, slope variance, concavity, distance to rocks and bathymetry that were scaled between 0 and 1, and collinearity was checked using the variance inflation factor (below four). Model evaluation was done on the validation dataset using the five performance metrics calculated for each model (10 GLMs): the continuous Boyce index (CBI,  Boyce et al.
2002 , Hirzel et al. 2006) , the area under the curve (AUC), the sensitivity, the specificity and the true skill statistics (TSS). The models goodness-of-fit was also assessed using the adjusted R 2 (marginal and conditional for the mixed models). Spatial autocorrelation of the regression residuals was tested using a variogram for each model as well as Moran's I test. To test the influence of different tracking durations and individual sample sizes on the model outputs, the dataset was reduced to 60 days of tracking and 120 locations per individual. The reduced dataset was then subsampled to two daily locations per individual. The performance metrics, response curves and predictions maps were then compared between the full and the reduced datasets to assess for spatial autocorrelation and test the influence of a subsampling procedure (Supporting information).
Model selection and performance Ten DC values ranging between 0 and 1 were tested for each turtle, but only seven were retained between 0. 004 and 0. 03, as values below 0. 004 were too low and those above 0. 03 too high to make the models converge properly. The CBI varied across the DC for the Individual models (Supporting information, based on the smaller CBI range across individuals, range: 0. 67-0. 99), the selected model had a DC of 0. 03. Both approaches showed very little sensitivity to pseudoabsences generation over the different runs. The values of the performance metrics were high (mean range: 0. 60-0. 99) with little variability (SD range: 0. 0005-0. 063) for both models (Supporting information). Despite its very good values (CBI > 0. 6, positive values indicate a model which predictions are consistent with the distribution of presences in the evaluation dataset), the CBI showed some variability for the Individual model (CBI range: 0.
67-0. 99). The Individual model had higher performance values for all metrics except for the CBI. Although their good predictive power, the lowest performance value was for the adjusted R 2 of the population model (mean: 0. 6). Despite a drastic reduction, some spatial autocorrelation was still present after the cross-validation subsampling but with a low sill of 0. 03 (Supporting information). Except for the CBI and sensitivity, the performance metrics were slightly higher for the reduced dataset of the Global model (Supporting information). For the individual model, all performance metrics were similar between the reduced and the full dataset (Supporting information).
Response curves The averaged models (from the 10 simulation runs) contained all environmental predictors for both approaches and all variables were highly significant (Fig. 3 , Supporting information). The individual approach also included the dispersal coefficient (DC optimal = 0. 03) and all variables were significant except the bathymetry that had no effect on the probability of turtle presence (p = 0. 892, Fig. 3 ). The relationship between turtle presence and the bathymetry was negative for the population model. The DC had a positive relationship with the probability of presence. For both approaches, the probability of turtle presence was positively correlated to the slope and the concavity, and negatively correlated to the slope variance and the distance to rocks (Fig  3 ). Response curves were similar for both the reduced and the full datasets (Supporting information).
Potential and realized distributions The map of the realized distribution of sampled individuals (based on maximum values) highlighted three main patches of high probabilities located around the three tagging sites (Fig. 4a ). The lowest probabilities were located where pseudoabsences were generated, and conversely, higher probabilities fit well the presence locations (Fig. 4b-d ). On contrast, for the potential distribution, suitable habitats were much more evenly distributed along the coast (Fig. 4e ). Given the strong inter-individual variability, the CV was lower for the potential compared to the realized distribution (Supporting information). The prediction maps were similar between the reduced and the full dataset (Supporting information).
A complementary analysis based on a reduced dataset using the same tracking duration and an identical sample size for each individual was also investigated in our study, and it showed similar results, confirming the robustness of our approach (Supporting information). In addition to classical performance metrics commonly used in SDMs, the CBI was also used here and enabled to assess how much model predictions differ from random distribution of the observed presences across the prediction gradients  (Boyce et al. 2002). The high CBI values derived from our models reinforce their good predictive performance  (Hirzel et al. 2006). The map of the realized distribution was also dependent on tagging locations. Three main tagging sites were used in this study based on prior investigation of the study area (ground truth data) in order to maximize the number of animals captured.
Acknowledgements -The analyses were carried out within the framework of the  NEXT  project with the support of  IFREMER ,  Institut Océanographique de Monaco , Fondation Albert I er and the  Prince of Monaco. The study was also part of the following projects: DYMATURE, HATOCAM and CHARC carried out by  IFREMER , Kelonia, CEDTM and IRD. This study was also undertaken within the framework of the  'National Action Plan for Sea Turtles  in the French territories of the south-west Indian Ocean'. The authors also thank the logistical support of  OceanObs, BNOI/ONCFS  and  Réserve Naturelle Marine de La Réunion , and to the staff of the  Délégation Océan Indien of IFREMER. We are also grateful to  Benjamin Robira  for its help on the analyses,  Pierre Lopez  for designing the conceptual scheme and  George Hughes  for helping with the English.
Data availability statement The environmental data is available from Sextant Digital repositiory <https://doi. org/10. 12770/7b316584-a52a-49c5-a819-9b78bb9f2159>. Also the location data of the turtles is available from Sextant <https://doi. org/10. 12770/d583c380-af34-44cf-9f75-9afee2a6dacc>. (Chambault et al. 2020b ).
Acknowledgments This study was financed by the  Guangdong Major Project of Basic and Applied Basic Research  (grant number  2020B0301030004 ),  National Natural Science Foundation of China  (grant numbers  31971458 ,  41971275 ),  Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)  (grant number  311021009 ) and the  Special High-level Plan Project of Guangdong Province  (grant number  2016TQ03Z354 ).
Data Availability Statement All data used in this study are publicly available. MODIS EVI data (MYD13C2) is available at https://modis. gsfc. nasa. gov/. CSIF data is available at https://figshare. com/articles/dataset/CSIF/6387494. TWS data is available at https://figshare. com/articles/dataset/GRACE-REC_A_reconstruction_of_climate-driven_wa-ter_storage_changes_over_the_last_century/7670849. SWdown data from CRUNCEP is available at http:// rda. ucar. edu/datasets/ds314. 3/. ERA-Interim data used to calculate VPD is available at https://apps. ecmwf. int/datasets/.