dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Sources Two main sources of data were used. The first dataset, called ObsMer 3  ("Observation des captures en Mer"), is collected as part of an onboard observer program set up within the Data Collection Framework of the Common Fisheries Policy. The ObsMer program is carried out by Ifremer ("Institut Français de Recherche pour l'Exploitation de la Mer"), under the supervision of the Directorate of Fisheries and Aquaculture ("Direction des pêches maritimes et de l'aquaculture, " DPMA). ObsMer observers' primary duty is to register the length and weight composition of catches. Still, they have to report any bycatch event if they witness such events. ObsMer data on PTM cover 4, 484 hauls between 2004 and 2021, of which 82 were associated with a bycatch event of at least 1 and up to 50 common dolphins.
ObsMer data on pair-trawlers are an unrepresentative sample of hauls, largely because allowing an observer remains largely at the discretion of skippers  (Babcock et al. , 2003; Benoît and Allard, 2009). The second dataset provides monthly estimates of total fishing effort in each division. This dataset is generated from the algorithm SACROIS developed by Ifremer and integrates data from Vessel Monitoring System, log-books, and landing statistics (for boats longer than 18 m from January 1, 2004, and longer than 15 m from January 1, 2005; Système d'Information  Halieutique, 2017). SACROIS aims at (1) correcting errors that could exists in the integrated dataset due to recording or collecting errors and (2) reconstitute métiers during the fishing trip as they are not recorded in logbooks or fish market data  (Cornou et al.
, 2018). The SACROIS dataset provides the best available estimates of total effort, in DaS, between 2004 and 2020 (Table  2 ). There are also refusals from skippers due to administrative and security reasons. Skippers must file an application for authorisation to embark observers and even if they decide to file, the authorisation may be declined due to security reasons (e. g. , not enough room or rails not high enough). These two datasets are complementary for our purposes: ObsMer provides micro-level data on marine mammal bycatch at the resolution of hauls. From these data, bycatch risk may be estimated  (Luck et al. , 2020). Fishing trips effort data, on the other hand, are macro-level: they provide spatialised effort data at the scale of a whole fishing fleet. These population-level data on effort allows the post-stratification of bycatch risk estimated from observer data to obtain the number of bycaught dolphins  (Authier et al.
, 2021). Descriptive statistics of both datasets are displayed in Table  2. Used in tandem, both datasets allow using regularised multilevel regression with post-stratification to estimate cetacean bycatch from non-representative samples  (Authier et al. , 2021). Estimates were finally compared to strandings along the French Atlantic seaboard. The French Stranding Network, founded in the 1970s, is dedicated to the monitoring of marine mammal strandings along the shores of France (mainland and overseas). Around 400 trained volunteers are currently taking an active part in the network. These volunteers make the complete coverage of French coastlines possible. Standardised training of volunteers by permanent Observatoire Pelagis staff, which takes place two times a year, ensure the homogeneity, comparability, and standardisation of data collection procedures in the field.
Observatoire Pelagis is mandated by the French Ministry of Ecology to train and deliver authorisation to handle carcasses of marine mammals (which are all protected species under national law). It also collates the data and analyse it to inform on the status of marine mammal populations. Stranding data for the period 2004-2020 were used. Only common dolphins found with lesions diagnostic of bycatch in fishing gear were considered  (Kuiken, 1994)  as well as those stranded during multiple stranding events, or "unusual mortality events" related to lesions diagnostic of bycatch. Multiple stranding events were defined as high numbers of strandings occurring in a restricted area with a common cause of death. The threshold was defined at 30 cetaceans over 10 consecutive days recorded along a maximal distance of 200 km in the Bay of Biscay, and 10 individuals per 10 days per 200 km of coastline along the coast of the western Channel  (Peltier et al.
Modelling Bycatch Risk and Duration of Hauls Observation des captures en Mer data allow both bycatch risk and haul duration to be modelled. The two may be correlated as a longer towing time may result in an increased likelihood of bycatch, all else being equal. Bycatch risk is defined at the level of a haul. Hauls can differ in duration as skippers may target different commercial species at different times of the year. However, the population-level data on effort is aggregated and available as DaS, the metric currently used in international fora (e. g. , ICES Working Group on BYCatch, WGBYC). The number of hauls per DaS was also modelled from the ObsMer dataset in order to scale up bycatch risk per haul by the number of hauls per DaS. We modelled jointly bycatch risk, fishing duration of hauls, and the number of hauls per DaS of pair-trawlers flying the French flag at the week-level for each year between 2004 and 2020 (Table  2 ) and each ICES division (Figure  1 ).
Smoothness of Matérn correlation function D() denotes the Dirichlet distribution for modelling proportions (such that 3 l=1 prop l = 1) and GG() the Gamma-Gamma distribution for scale parameters  (Griffin and Brown, 2017; Pérez et al. , 2017). on the supercomputer facilities of the "Mésocentre de calcul de Poitou Charentes (Université de Poitiers/ISAE-ENSMA/La Rochelle Université). " Codes are available at https://gitlab. univlr. fr/mauthier/cdptmbycatch. For confidentiality reasons, the actual dataset cannot be shared: a synthetic dataset, generated by predicting from the posterior distribution, is provided instead.
Comparing Model-Based Estimates With Strandings The sample provided by ObsMer, a non-dedicated observer scheme of marine mammal bycatch, may not be representative of all bycatch. In addition, it provides very sparse data, with less than 100 observed events over 17 years (Table  4 ) when strandings have reached several hundred per week in recent years (ICES, 2020d) (for all causes of death). Despite this, the weekly pattern of bycatch risk provided by ObsMer roughly matches that of strandings, with an increase in winter (Figure  3 ). Despite this rough match, the ObsMer data also suggest a heightened risk in summer, especially in the 2000s, whereas strandings suggest such an increased risk in very recent years  (Peltier et al. , 2021). The number of stranded common dolphins with evidence of bycatch can be used to estimate the total bycatch mortality with reverse drift modelling  (Peltier et al.
RESULTS We built and compared six models (Table  4 ). Convergence was reached for all parameters with all R < 1. 05. Model M 6 had the lowest WAIC and was selected as the best model for Models are ordered in increasing order of W ÂIC (the smaller, the better the fit). se stands for "standard error". 4 ) and the dotted black line the yearly average (parameters ǫ t in Equation  4 ). further inferences. Model M 6 included an interaction between week, year, and ICES division (Equations 4 and 5). All codes to fit models are available at https://gitlab. univ-lr. fr/mauthier/ cdptmbycatch.
Within-Year Variations in Bycatch Risk We uncovered the within-year pattern in bycatch risk of common dolphins. Bycatch risk is the highest in winter, during the first weeks of a calendar year. This pattern is largely congruent with the pattern seen in strandings of common dolphins in the Bay of Biscay  (Gilbert et al. , 2021). Both stranding and observer data, which are independent, identified 2017 and 2019 as years with the highest risk of bycatch  (Gilbert et al. , 2021; Peltier et al. , 2021). A limitation of stranding data is how the location of bycatch events must be inferred with reverse drift modelling  (Peltier and Ridoux, 2015). The ObsMer data in contrast included geolocalised bycatch events, with a spatial resolution at the level of ICES divisions kept for analysis. Despite this coarse resolution, we could identify divisions 8.
a and 8. b as the ones with the highest risk of bycatch by PTM. The ICES Working Group on Bycatch (WGBYC) estimate bycatch of protected species, including common dolphins, in the North East Atlantic. Using data collected by onboard observers collected between 2005 and 2017, bycatch rates for ICES divisions on the continental shelf of the Bay of Biscay were estimated with ratio estimators (ICES, 2019). These estimates are not produced at the week level, but ICES (2019) also identified divisions 8. a and 8. b as the ones with the highest of bycatch in midwater trawls for common dolphins over the period  2005-2017 (p. 61). ICES (2019) estimated yearly rates ranging between 0. 285 and 0. 372 dolphins per DaS and warned against extrapolation given the low observer coverage (p. 61). Our model-based approach overcomes this limitation  (Authier et al.
This improvement will require accomodating a large overdispersion, but there were however less than 100 such events in the dataset and we chose to use the median. This is a cautionary choice since the median is less sensitive to the few events for more than 10 dolphins. The uncertainty in the median number of dolphins involved in a bycatch event is currently ignored: incorporating it in future development will further widen credibility intervals (which are already large;  Authier et al. , 2021). Thus, the model-based estimates are conservative estimates of bycatch by PTM. Bycatch risk was also very variable between years: the large between-year variations may be due to ecological factors. Bycatch risk results from both fisheries activity within a particular division at a particular time and dolphin presence.
| Data Data were collected with butterfly monitoring schemes carried out in Finland, Spain (Catalonia) and the UK. The schemes consist of a network of sites where volunteers count butterflies along transects following a standardized framework called the "Pollard walk"  (Pollard & Yates, 1993). We then processed counts using generalized additive models (GAMs) to provide an index of abundance per site and year  (Dennis et al. , 2013; Schmucki et al. , 2016). In the case of missing counts, we interpolated from the GAM fitted to counts made at other sites in the same bioclimatic zone  (Metzger et al. , 2013; Schmucki et al. , 2016). This approach generates unbiased estimates of abundance and performs better than interpolations from simple linear regressions  (Dennis et al. , 2013). However, to assure robust estimates, we removed indices of abundance with >50% of missing observations  (Schmucki et al.
, 2016). The schemes started in different years, and the number of sites per region varied: Finland (1999, number of sites = 107), Spain (1994, number of sites = 130) and the UK (1976, number of sites = 2128). Therefore, Finland, the country with the shortest scheme, set the study period  (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017). From each of the schemes, only those sites with >10 years of data were retained, leaving 59, 55 and 762 sites from Spain, Finland and the UK, respectively. The data included 157 species, and Spain had the highest species richness and the highest number of species (S) unique to the country (S = 131, unique = 74), followed by Finland (S = 58, unique = 12) and the UK (S = 58, unique = 8).
We converted counts into densities by dividing the indices of abundance by the transect length, thus standardizing the measure of abundance across transects of different lengths. Temperature data were obtained from the European Climatic and Assessment Dataset (ECAD) project  (Haylock et al. , 2008; Klok & Klein Tank, 2009). ECAD provides gridded daily temperatures at a 0. 1° scale through interpolations from observations collected by a network of meteorological stations. The temperature was collected to the nearest degree at each site from the period of 1999-2017.
The thermal range preserved the relative difference between the temperatures at the sites where a species was observed but standardized thermal range differences between species, hence a score of +. 75 covers 87. 5% of the total projected temperature range for a species independent of the absolute size of the temperature range. Note that the temperature range across all our monitoring sites from Catalonia (Spain) to Finland will not always capture the full thermal range for every species (e. g. , some extend beyond the (1) Thermal range position ij = 2 T ij -min(T) ∕ max(T) -min(T) , southern Mediterranean region into Africa), but the standardization reflects the relative thermal range position in our sampled dataset.
Information on priors is provided in the Supporting Information (Appendix S1). All models were constructed in R v. 4. 1. 1 (R Core Team, 2021). The hierarchical Bayesian models were fitted using the "INLA" package  (Lindgren & Rue, 2015) , and the package "brinla" was used to aid in diagnostics  (Faraway et al. , 2021). Polygons for barrier models were taken from the "rnaturalearth" package  (South, 2017) , and spatial fields were plotted using functions from the paper by  Krainski et al. (2018). Code supporting the results is archived at DOI: 10. 5281/zenodo. 6350070.
, 2003)  or DSSAT  (Jones et al. , 2003)  have been embedded in the HarvestChoice (2010) platform to nourish regional-scale decision-making. WOFOS  (Wolf and van Diepen, 1995; Reidsma et al. , 2009; Supit et al. , 2012)  was reformulated into the modular Python Crop Simulation Development programming structure  (de Wit, 2015). Gaudou et al. (2013)  have integrated AqYield  (Nolot and Debaeke, 2003; Murgue et al. , 2014; Constantin et al. , 2015)  into the MAELIA multi-agent platform. Several studies have shown the usefulness of coupling cellular components representing a landscape with an agent-based simulator at the household or farmer level with behaviors that alter the landscape  (Parker et al. , 2003; Verburg et al. , 2004; Berger et al. , 2006). Such approaches enable assessing the environmental, economic, and social impacts of the combined changes in agricultural activities (demography, dynamics of land cover and climate).
During the irrigation campaign, the possibilities for temporal adjustment of irrigators' calendar are thus limited by these spatial constraints and depend on the behavior of other irrigators influencing the network flow rate. In particular, irrigation coordination is a key factor for sharing water resource that must be distributed through a common network in the right place and time to all along the irrigation campaign. In this context, our simulation approach aims at capturing the potential impacts of spatial (i. e. network flow gradient) and temporal (i. e. network coordination) constraints due to a gravity irrigation network on plant LAI and WSI dynamics. Integrated simulation approaches that merge models into a unified software package (e. g. Matthews, 2006; Schreinemachers et al. , 2007; Gaudou et al.
, 2013)  are useful to capture the complex human-environment interactions and shed light on the understanding of the system dynamics with potential entry-points for policy design  (Dragan et al. , 2003). However, model simplifications commonly occur when merging modules, especially of environmental processes (e. g. Schreinemachers et al. , 2007 Schreinemachers et al. , , 2010)) , altering the model's ability to fully capture interactions. A more flexible method called co-simulation, also used by scholars in farming and environmental modeling (e. g. Warner et al. , 2008; Bithell and Brasington, 2009; Bulatewicz et al. , 2010) , consist in coupling simulation software together rather than integrating individual components in a single modeling and simulation software. The main advantage is that there is no need for recoding components and consequently the users and developer's community of each model component may be asked to help with the coupled model (control code, provide and test new modules,.
). However, it involves conceptual and programming developments to ensure compatibility of many characteristics including basic assumptions, spatial and time scales. First, it requires to make sure that models components and associated software have communication features and manage disruption correctly. Last but not least, it requires a clear conceptual approach to organize scheduling between the models and between the simulation software. The objective of this paper is to present a co-simulation approach based on the coupling of the Optirrig crop model at the plot level with the WatASit agent-based model at the irrigation network level. As a proof of concept, we applied the approach for capturing the potential impact of a typical gravity-fed network in the South-East of France on 16 cereal plots.
Overview The purpose of the co-simulation is to capture the potential impacts on crop LAI and WSI dynamics induced by the gravity irrigation network when operating irrigation during a collective irrigation campaign. The approach consists in linking a crop model at the plot level, and an ABM at the network level, based on the daily coupling of their two simulation software described in Table  1. It uses the Optirrig crop model for simulating LAI and WSI as a proxy for crop development and water stress, and the WatASit ABM at the network level for generating irrigation operations under four progressive levels of collective irrigation constraints (see Section 3. 6). Optirrig simulates climate-soil-plant interactions daily in which irrigation dates (I+) on each plot are forced by WatASit through a daily coupling (Fig.
Technical features Co-simulation involves the communication of several model components that include their solvers (meaning that the components can compute of the effects of external events and time advance). These components are viewed as slaves' processes and are orchestrated by an external program referred to as a master algorithm. The co-simulation approach has required then the development of specifics Application Programming Interface (APIs) designed as sets of R functions, so that the model components that we used can be driven as slave processes by the master algorithm. The first API called RCormas 1  is based on the HTTP protocol and allows communication between the R software in which the master algorithm is implemented and the COR-MAS platform running the ABM. We will call the second API the Roptirrig that enable the master algorithm to communicate with the Optirrig-D simulator already coded in R.
The total simulation period runs from 15 October or Day of Year 1 (DOY 1), when the winter cereals are sown, to the end of the cereal season on July 31 (DOY 289). The co-simulation starts at the beginning of the irrigation campaign on May 1st (DOY 198) when irrigators of Aspres-Sur-Buëch water their network each year. 2017 is taken as a reference for the climatic forcing. Simulation output (i. e. dates of irrigation, LAI, TT, and WSI on each farm plot) are collected from the two models by recording attributes of WatASit-Aspres entities (i. e. irrigated state of farm plot entities) and Optirrig-D variables (i. e. cereal LAI, TT, and WSI) in the R software during the irrigation campaign. In Sections 4. 3 and 4. 4, we also indicate an output from an extreme simulation run called "NoIrri" without any irrigation input during the irrigation campaign, as a benchmark.
Limitations of the co-simulation approach As described by  Letcher et al. (2013) , the coupling of individual models is only possible under certain conditions. First, individual model components do not necessarily work on the same space and time scale, but disaggregation or aggregation must often be applied to link models. In our approach, the water network results from aggregation in the ABM of the elementary spatial entities (the plots). In addition, the ABM has also a double-time scale (hourly and daily). These double space and time scales make it possible to link daily irrigation at the plot level in the crop model, with the constrained operations at the network-scale. Specific protocols and associated computer software and API exist to develop and merge models components in integrated development environment.
Acknowledgments The authors would like to thank the  Zone Atelier Bassin du Rhône (ZABR)  and  Agence de l ′ Eau Rhône-Méditerranée-Corse (AERMC), France , for funding the  RADHY Buëch  project to which this work contributes. We also thank  Météo-France  for providing the SAFRAN reanalysis. The authors are grateful to the irrigators of  Aspres-Sur-Buëch, all french State Services , and associated structures of the  Hautes-Alpes County ,  Claire Distinguin , for providing help during field surveys, and  Jean-Claude Mailhol , for his explanations of the PILOTE model. Finally, the authors thank the anonymous reviewers.
, 2018). Given the reduced significance of sowing, in this paper, only the grazing factor was considered. During the project, one ungrazed plots was excluded from analysis. In late June 2019, the cover of all vascular plant species within each 1 × 2 m subplot at the center of the experimental plots was estimated using a point quadrant method with a density of 100 pins/m 2  (Moore & Chapman, 1986). All species touched by each pin were recorded so that the total species cover within each 1 × 2 m subplot can exceed 200 pins. Species present in a plot but not touched by any pin were recorded with an arbitrary cover of 0. 5 pins. All species abundance data are available in Appendix 2. A set of functional traits was measured for the most abundant species in both treatments (8 grazed plots and 7 ungrazed plots).
These distances were then linearly rescaled by dividing each distance by the maximum value found in both matrices. We then used the scaled functional distances and the species relative abundances in each plot to calculate Cor B , SES B and Q B. All calculations were performed with a new R function available in an Electronic Appendix to this paper (see Supplementary data, Appendix 3). Starting from version 2. 1. 2, the R function will be also available in package adiv  (Pavoine, 2020) : https://cran. r-project. org/web/packages/adiv/index. html. The values of Q B were estimated based on 10,000 random permutations, which is a manageable subset of the total number of N! possible permutations. Additional work is ongoing to find an analytical or algorithmic solution to the exact calculation of Q B.
Data collection In structured standardized face-to-face intensive interviews, study participants provided information regarding their medical history including previous or current diseases, neurological conditions, neuropsychiatric symptoms, current medications, hospitalizations, surgeries and injuries. Medical records of previous diagnoses, physician visits, or hospitalizations were inspected for all participants. Additionally, an extensive structured and standardized physical examination was conducted, evaluating neurological signs and symptoms. Structured questionnaires were used in order to gather information about participants' functioning, social, mental and physical activities, as well as sleep and dietary habits. Sociodemographic information and information about tobacco use was also collected.