dataesr/scientific-paragraphs-categorization huggingface dataset:
Extra battery life was provided using an off-the-shelf power bank, enabling 10 hr of filming. The fully equipped infrared camera weighed 400 g (0. 6% of the average animal's live weight). For georeferencing sheep locations, GNSS receivers weighing 22 g (Mr. Lee CatTrack 1 , Catnip Technologies Limited, Anderson, SC, USA) were used. Positions were recorded at 5 min intervals, and GNSS receivers were replaced weekly  [39]. Geospatial software ('@trip pc' (http://www. a-trip. com) was used to analyse, edit, restructure and filter GNSS tracks.
Behavioural analysis Observed behavioural states are described in the ethogram (S2 Table ) and were considered mutually exclusive. Instantaneous scan data of flock behaviour (a total of 45 hr of direct observation) were used to calculate time-budgets, that is, the proportion of time spent in each behavioural state. To represent behaviour over an entire hour, 10 min scan data were multiplied by 6. Durations (per hour) of each behaviour were converted to a percentage of the total time. Focal data of individual behaviour (POV cameras) resulted in a total of 107 (~290 h) video sequences in Experiment 1 and 45 (~123 h) in Experiment 2. Video sequences were independently analysed by three observers. The intra-observer and inter-observer reliability of the video behavioural analysis was determined using the same three randomly sampled videos per week (each in a different patch and with a different animal).
Relative reliability was assessed via the intraclass-correlation index and the coefficient of variance (n = 33; 0. 67; 0. 72). The full video sequences analysis provided three sets of behavioural data: patterns of grazing, measured as time-budgets of behavioural activities; structure of intake, measured as the number and frequency of events; and diet selected, measured as feeding niches allocated to feeding stations. Behavioural states were extracted from video sequences through continuous focal sampling using Behavioural Observation Research Interactive Software (BORIS,  [40] ), a free and opensource event logging software. The full video sequences were analysed using 10 sec intervals to reduce the workload. Whenever the animal changed behavioural state, the video was rewound to pinpoint the exact second to record behaviour continuously.
Additionally, whenever the start of a feeding station was logged, the corresponding feeding niche selected by the animal was registered. For each video sequence, the software's output provided the time-budgets, sequence, total duration, and total frequency of each behaviour. This data set was further organized hierarchically into grazing, meal, bout, and station patterns, and the structure of each behaviour level was determined, namely the size, duration, and rate (Fig  1). At each behaviour level, activity was separated by intervals (Fig  2 ). The majority of intervals between meals were longer than 60 minutes, while intervals between bouts were usually longer than 5 minutes but shorter than 60 minutes. We used the hour as the time unit for analysing bout patterns, similar to  [41]. In addition to grazing pattern and structure, the video sequences revealed the diet composition, which was estimated from the proportion of feeding niches containing grasses, legumes, and forbs in the total feeding niches used during a meal.
Statistical analysis Repeated measures data were analysed using mixed models, with date and time periods as fixed effects, and animals as random effects (SPSS 22. 0 software, IBM Corp. , Armonk, NY, USA). Variables were tested for normality (Shapiro-Wilk) and homoscedasticity (Levene). When the assumptions were not validated, non-parametric tests were used (Kruskal-Wallis, Spearman's correlation). To compare behavioural variables among dates (early spring and late spring 2016, and early spring 2017), weeks were grouped by season (early spring: first 5 weeks; late spring: last 5 weeks). Although a 1-week gap between seasons was used to create two balanced clusters of 5 weeks (used in one-way analysis of variance [ANOVA]), all the tables show all data set (11 weeks). Means were compared using the Bonferroni post-hoc test.
Two-way ANOVA was performed each year to evaluate the effects of date or time period on behaviour variables. Means were compared using Tukey's test. Individual grazing behaviour and group grazing behaviour were compared using Spearman's rank correlation and t-test (SPSS 22. 0 software, IBM Corp. , Armonk, NY, USA). The threshold of statistical significance was set at P < 0. 05 for all statistical analyses.
Within the European Joint Program on Human Biomonitoring HBM4EU, human biomonitoring guidance values (HBM-GVs) for the general population (HBM-GV GenPop ) or for occupationally exposed adults (HBM-GV Worker ) are derived for prioritized substances including dimethylformamide (DMF). The methodology to derive these values that was agreed upon within the HBM4EU project was applied. A large database on DMF exposure from studies conducted at workplaces provided dose-response relationships between biomarker concentrations and health effects. The hepatotoxicity of DMF has been identified as having the most sensitive effect, with increased liver enzyme concentrations serving as biomarkers of the effect. Out of the available biomarkers of DMF exposure studied in this paper, the following were selected to derive HBM-GV Worker : total N-methylformamide (tNMF) (sum of N-hydroxymethyl-N-methylformamide and NMF) and Nacetyl-S-(N-methylcarbamoyl)cysteine (AMCC) in urine.
In 2018, IARC re-evaluated DMF as probably carcinogenic for humans (Group 2A) based on limited evidence in humans and sufficient evidence in experimental animal studies  [10]. DMF is not categorized for its carcinogenic properties in the European Union. For the present work, a review of health effects associated with DMF exposure was conducted to provide an HBM-GV Worker for occupationally exposed people, with respect to the current methodology set in the HBM4EU project  [2]. To achieve this, an assessment of the extensive database on DMF (including toxicokinetic and toxicodynamic data) was performed.
(1997) , who treated lactating rats with a single oral dose of 100 mg/kg bw [ 14 C]-DMF on lactation day 14, measured DMF, HMMF, and NMF in the rats' milk at concentrations equal to those in the plasma. In addition, they noted that 60-70% of the absorbed dose of [ 14 C]-DMF was excreted in the urine and 3-4% in the feces  [29]. There is no study evaluating the pulmonary elimination, possibly due to strong adsorption of DMF on the walls of the measuring device  [22]. A recent overview of key studies on toxicokinetic data in humans and animals is presented in ECHA's opinion on DMF restriction  [7]. Based on all these data, several metabolites can be identified as potential BME for biological monitoring of DMF at the workplace: -Unchanged DMF in urine; -total NMF or tNMF (which is the sum of HMMF and NMF) in urine; -AMCC in urine; -MCVal in blood; and formamide in urine.
Identification and Characterization of the Dangers Associated with DMF Exposure As the HBM4EU strategy for deriving an HBM-GV recommends giving priority to human data for the characterization of the relationship between the risk of adverse effects and internal concentrations of the selected biomarker(s), only human data were initially considered. This initial retrieval provided a sufficiently extensive database on the effects on humans, allowing the identification of HBM-GVs. In published human studies of health effects, DMF exposure was mostly assessed via airborne DMF measurements and/or via biomonitoring (mainly tNMF and AMCC in urine, and less often MCVal in blood). The following effects have been studied: acute toxicity; skin, eye, and respiratory tract irritation; chronic toxicity (effects on liver, gastro-intestinal effects, and alcohol intolerance); genotoxicity; carcinogenicity; and reproductive and developmental effects.
Discussion Literature on DMF biomonitoring offers a large database on robust relationships between health effects and urine levels of tNMF, and to a lesser extent of AMCC, the critical effect being hepatic damage (characterized by an elevation of serum hepatic enzyme activity). Limit values have previously been proposed by SCOEL (2006), ACGIH (2017), and DFG (2019) for tNMF and AMCC in urine of workers exposed to DMF  [16, 19, 20]. However, a recent study by  Wu et al. (2017)  identified a lower threshold value for hepatotoxic effects than those previously identified by SCOEL, ACGIH, or DFG for tNMF concentration in urine (15 mg•L -1 to 30 mg•L -1 : see Table  4 )  [46]. Moreover, in its opinion on a restriction dossier for DMF  [7] , the RAC preferred deriving the DNEL inhalation value for workers from the Kilo et al.
(  2016 ) study  [51] , which was also not taken into account for the evaluations by SCOEL (2006)  [20]  and ACGIH (2017)  [16]. This background justified a new evaluation of the published data for the derivation of guidance values for tNMF and AMCC concentrations in urine of workers. The present evaluation identified a pool of studies for the derivation of an HBM-GV Worker for tNMF in urine. From the results provided by these studies, an HBM-GV Worker of 10 mg•L -1 (or 10 mg•g -1 •cr) was agreed on and is hereby recommended. Considering tNMF half-life time, end-of-shift urine sampling at any day of the workweek can be performed for this biomarker. In addition to this recommendation, an HBM-GV Worker has been derived for AMCC which is an indicator of cumulative exposure after several days.
Regarding the critical endpoint and mode of action The confidence in the evidence of effects on the liver function is high. The effects on the liver after DMF exposure are well-studied in humans (workplace) and animals. LoC: High The confidence in the evidence of effects on the liver function is high. The effects on the liver after DMF exposure are well-studied (in humans and animals). LoC: High Regarding the selected key studies for identification of the POD and their results The database gives several robust occupational studies with many subjects and consistent results for tNMF. The approach consists of the selection of a pool of studies (from 1980 to 2017) carried out on Asians and Caucasian people (to consider the genetic variability due to ethnicity).
Reproductive traits Reproductive performances of Large White purebred sows were extracted from the national database for litters born between January 2014 and October 2020 in nucleus herds of the breeding companies AXIOM (Azay-sur-Indre, France) and NUCLEUS (Le Rheu, France). Sow reproduction traits comprised the number of piglets born alive (NBA), the number of stillborn piglets (NSB), the number of weaned piglets (NWP) and the piglet mortality during the suckling period expressed in percentage (PM = 100 * (NBA À NWP)/N BA). Stillborn piglets were identified by farm operators either as delivered dead during the farrowing process or found dead within the membranes behind the sow after farrowing is complete. In purebred litters only, all live-born piglets were individually weighed on the day of birth (66%) or on the following day (34%).
These individual birth weights were used to estimate litter traits: the mean birth weight (MBW), the CV of piglet birth weights (CVBW), the litter weight at birth (LWB), and the proportion of live-born piglets lighter than 1 kg (PPL1K). The LWB and PPL1K performances were discarded for litters in which the number of weighed piglets at birth was not equal to the number of born alive piglets (13% of the litters). Because the absence of a birth weight for a single piglet impacts very little MBW and CVBW of litters with more than 10 piglets, MBW and CVBW performances were kept for litters with more than 10 piglets in which only one birth weight was missing. This led to removing 6% of MBW and CVBW performances from the initial dataset. All performances that were three SDs beyond the phenotypic mean were discarded.
Moreover, only sows with valid performances for NBA, NSB, NWP and PM were kept for statistical analyses. Finally, performances recorded in farrowing batches with less than 18 sows were removed from the dataset. In total, litter size and piglet mortality records were available for 21 719 sows, having 2. 8 litters on average, and 59% of these litters were purebred. Descriptive statistics for sow reproduction traits are summarised in Table  1.
Growth, feed efficiency and digestibility coefficients The dataset comprised feed efficiency and digestive efficiency traits recorded on 4 643 non-castrated Large White male pigs tested since 2015 in 88 successive batches at the INRAE UE3P -France Génétique Porc phenotyping station (Le Rheu, France, https://doi. org/10. 15454/1. 5573932732039927E12). These pigs had dams and half-sisters with reproductive performances, and some full sisters. A fraction of these animals (n = 1 391) also had digestibility coefficients predicted for energy, organic matter and nitrogen and corresponded to the experimental design described by  Déru et al. (2020 and 2021). Piglets arrived at the phenotyping station just after weaning at three weeks of age. They were allotted in groups of 14 animals. These groups remained unchanged from postweaning to slaughter.
Z is the incidence matrix of the additive genetic effects. perm $ N(0, I r 2 perm ) is the random effect of the permanent environment of the sow, and T is the incidence matrix relating performances to the random effect perm. Finally, e $ N(0, I r 2 e ) is the residual random effect, and I is the identity matrix of appropriate size. Because low genetic Â diet interactions were estimated for all production traits, including DC traits  (Déru et al. , 2020 and 2021) , all pigs fed the conventional and high fibre diets were kept in the subsequent statistical analyses to maximise the amount of available data and hence the accuracy of estimates. Production traits recorded with both diets were considered as single traits, and diet was modelled as an environmental factor of variation. Production traits were analysed with the following animal linear mixed model: y ¼ X þ Zu þ Wpen þ Vc þ e; where y is the vector of phenotypes for a given trait, b is the vector of fixed effects depending on the trait considered: diet, batch and DFI within diet for DC traits, as recommended by  Déru et al.
For instance, growing pigs with DC traits had on average 34. 7 ± 37. 9 half-sisters with at least one NBA record and 18. 1 ± 20. 9 half-sisters with at least one MBW record. The pedigree relationship matrix was constructed tracing back five generations upwards and considering altogether growing pigs and breeding sows with performances. The pedigree file contained in total 32 326 individuals. Variance components were estimated by Restricted Maximum Likelihood using the ASREML 3. 0 software  (Gilmour et al. , 2009).
, 2004; Dourmad et al. , 2021). Besides, the routine use of ingredients containing more dietary fibres in sow diets, to favour satiety and welfare under restricted feeding, certainly enhances the importance of better digestive efficiency, as compared to growing-finishing diets that contain fewer dietary fibres. Therefore, these results highlight the fact that variability in digestive efficiency of sows certainly exists and should be accounted for to define more adequate feeding plans. This study was based on an experimental dataset of limited size designed to assess the genetic variability of DC traits  (Déru et al. , 2021). Hence, genetic correlations were estimated with moderate accuracy, in particular between DC traits and other production traits, and must be considered with caution. Compared to the gold standard methodology used to measuring digestive efficiency, the NIRS-based methodology considered in this study makes it possible to phenotype large numbers of animals reared in groups.
Thus, it should facilitate collecting larger datasets in future to refine estimates of genetic correlations. In future research, it will be also critical to finely measure feed intake and DC traits in females during both the growing and the reproductive stages, to be able to disentangle genetic and environmental effects influencing feed and digestive efficiency, and litter characteristics. In addition, measuring digestive efficiency traits in lines divergently selected for improved piglet survival could be a way to evaluate experimentally if an increase in digestive efficiency is observed as a correlated response to selection. To conclude, data were collected in two large groups of closely related animals that were representative of the genetic variability existing in two connected French Large White commercial populations.
Moreover, even though one can determine with these methods whether a pixel belongs to a chicken or to the ground, it is still complicated to determine which chicken it belongs to when two animals are close to each other. Faced with the limitations of classic segmentation methods, for example, in the case of higher animal densities, researchers now use AI. Supervised AI allows learning by the machine by showing it thousands of labelled and categorised examples. In this way, the machine becomes capable of correctly classifying most of the images it is shown  [153]. A database of characterised images is needed for learning the model, but it is most useful when the model is deemed functional. AI-based detection is much more robust and faster than conventional methods. The major limitation of AI is that more powerful and highperformance machines are needed to allow for great numbers and sometimes more complex calculations.
Furthermore, these tools also need to incorporate precise monitoring of health and welfare to promote a global approach to health and welfare monitoring. Strong support from public authorities is needed to tackle this complicated problem and to reach an agreement on the objectives, appropriate indicators, pre-existing tools to be reused (if existing and adapted to produce the indicators), and interconnection of existing databases. Further research projects involving stakeholders are also required to develop and test new tools using both applied and research perspectives to analyze in detail the synergies and trade-offs that exist between health, welfare, and antimicrobial use at different scales (animal, flock, and farm). The monitoring of antimicrobial use at the farm level has already been implemented for years in different species and countries (a review in  [16] ).
Third, sensors are exposed to errors or drift that would be impossible or difficult to identify. Finally, the use of sensor data to assess animal welfare requires the development and validation of algorithms and a procedure for using them. In addition to sensor data, several other sources of data are likely to be available on-farm, such as milking records (e. g. milk quality), grassland management data (e. g. height of pre-and postgrazing sward), and veterinarian records (e. g. in-herd disease prevalence). These information sources, summarised at the farm and season level, could in turn serve to inform the assessment of the overall welfare of animals on that farm. Questions on the prevalence of some diseases, for which grazing cows are more at risks than indoor cows (e. g. prevalence of diseases due to ingestion of toxic plants), must also be added in the farmer interview.
g. within the criterion 'Expression of social behaviours', all measures, numbered from 37 and 38, must be performed if possible) whereas measures with the same number can replace each other (e. g. within the criterion 'Good human animal relationship', the two measures numbered 40 will potentially afford the same information). b DEV-M = development of a method for behavioural observations and determination of the ethogram; DEV-A = development of algorithms for data analysis; TRU = check of trueness and selectivity; REP = check of repeatability; STA = check of stability over time; SEN = check of sensitivity; THE = determination of thresholds. I Measure from the Welfare Quality protocol. II Measure from the FARM protocol. * Measure to be performed with sensors.
Statistical analysis The proportion of foals that first approached their dam rather than the familiar mare was compared to the null hypothesis (no preference; a theoretical threshold of 50%) with a two-tailed Z test. Because the behavioural data were not normally distributed, nonparametric statistical tests were used. Comparisons of the foal behaviours directed towards their dam and the familiar mare were analysed by a Wilcoxon signed-rank test, which is used to analyse related ordinal data. The effect of foal sex (male vs female) was tested with the Wilcoxon two-sample test for non-related samples (equivalent to a Mann-Whitney U test). The analyses were performed in XL Stat (Addinsoft, 19. 4) and SAS statistical package (SAS 9. 4. , SAS Inst. , Inc. , Cary, NC).
Descriptive and statistical analyses In total, 32 answers to our questionnaire were analyzed. Sociodemographic variables were quantitative and qualitative. The other variables extracted from the questionnaire were derived from 20 close-ended and seven open-ended questions. For descriptive analysis of the variables, graphic quantifications were conducted in Excel 2016 using dynamic tables. XLSTAT 2021. 4. 1 (Addinsoft, 2022)  was used for a series of statistical tests. The normality of the distribution of men's and women's age pyramids per country was assessed through a Shapiro-Wilk test (alpha = 0. 05), and was rejected in all cases except female vendors from Benin (data not shown). Differences in distribution were compared using a non-parametric Kolmogorov-Smirnov test (alpha = 0. 05). Linear regression analysis between age and seniority was conducted to assess the correlation between the two variables.
2021a). Gendered structures could be important to consider in the bushmeat trade dynamics as, together with ethnic group selection, they may lead to differentiated responses to a same constraint. Whether gender influenced perceptions on the measures against the COVID-19 pandemic was not directly tested in our study but could be further investigated using our database and future surveys. One striking pattern was the significant, positive correlation between age and seniority among the bushmeat vendors. To our knowledge, this is the first time that a study demonstrates on a large scale (West Africa) that being a bushmeat trade vendor means engaging into a stable career. Such a pattern may be at least partly explained by the overall low level of education observed among vendors, low education -coupled with family-and/or ethnicity-fostering the will to build long-term careers in the bushmeat trade  (Randolph et al.
Acknowledgments Fieldwork in Côte d'Ivoire, Benin and Cameroon , and the co-authors PG, SGB, CD, ADM, and AA, were supported by  ANRS-MIE  ( AFRICoV -ANRS COV6 ). KJG and SGB were also supported by the  Ministère de l'Enseignement Supérieur et de la Recherche Scientifique (MESRS) of Côte d'Ivoire , as part of the " Trace-Brousse "  research project (Franco-Ivorian Cooperation  within the framework of the Second Contract of Debt Reduction-Development Contracts -C2Ds) and  National Research Foundation (South Africa ) grant UID:133615/Nr:  COV19200617532886. KJG received a PhD scholarship from " Appui à la Modernisation et à la Réforme des Universités et Grandes Ecoles de Côte d'Ivoire"  ( AMRUGE-CI_N°2 ). PG and AA received funding from  BUSHRISK  ( FCT IC&DT 02/SAICT/2017-n° 032130 ). PG also received funding from the  IRD -Department ECOBIO.