dataesr/scientific-paragraphs-categorization huggingface dataset:
Labeling of accelerometer data Visual comparison of the start times of recorded behaviors with the raw accelerometer data for a subset of the data revealed that the recorded start time lagged behind the accelerometer signatures. Therefore, we applied an offset to all behaviors (1 s for data from Alaska, 2 s for data from Norway). The non-overlapping 3-s accelerometer data intervals were labeled with the respective behavior recorded during the observations. Intervals during which more than one behavior was recorded were excluded from analysis. The frequency with which different behaviors were observed varied greatly. Because our goal was to obtain a model that could reliably distinguish the main behaviors of moose, we excluded rare behaviors such as head shaking, scratching and urinating, which represented 2.
Predicting behaviors from accelerometer data To classify the accelerometer data into behavioral categories, we used a random forest algorithm, which is frequently used for the classification of accelerometer data  [23, 47, 63]. A random forest grows many decision trees on bootstrapped subsamples of the data and combines the predictions of all trees to predict the out-ofbag data that were not used to grow the trees, in order to quantify prediction error  [64, 65]. Random forest is a comparatively fast supervised classification algorithm that, through the combination of many decision trees and introduced stochasticity in the modeling process, increases classification performance and can process correlated and interacting predictor variables as well as missing values  [64] [65] [66] [67]. To accommodate the unbalanced nature of our dataset, we assigned weights to the observations of each behavior that were inversely proportional to the class size of the respective behavior (i.
However, individual length and girth had comparatively low variable importance in our model. Other variables such as age or weight might have been more important  [25]  but were not included in our model because these metrics are difficult to determine in the field when collaring wild moose. Furthermore, length and girth were not measured on all animals in our study and were inferred from other data for several individuals, potentially confounding the importance of these metrics on the behavioral classification of moose accelerometer data. Fine-scale differences in placement of the accelerometers among individuals might have contributed to the individual variation in the classification performance of our model  [48, 69]. Because most collars were deployed for several months at a time, they were fitted to account for seasonal changes in neck diameter, potentially resulting in changes in how the collars responded to body movement over the course of the deployments, thereby increasing within-and among-individual variation of the data  [22, 38, 85].
Funding Financial support for this study was provided by the  Alaska Department of Fish and Game (ADFG) Federal Wildlife Restoration   Grant  (Grant number  AKW-4 Project No. 1. 63 ). ADFG purchased the collars used in Alaska. The Innlandet country council and the municipalities Åsnes, Våler,  Elverum  and Åmot in Norway provided funding for purchase of the collars used in Norway. TK was supported by an  Erasmus+ traineeship mobility grant.
Statistical Analysis Statistical analyses were performed using Statgraphics Centurion XVII version 17. 1. 04 software. Significance of results differences were determined with one-way analysis of variance (ANOVA, p value < 0. 01) and a multiple comparison procedure (Fisher's leastsignificant-differences (LSD)). The estimated probability of detection with 50% and 95% confidence (LOD 50 and LOD 95 ) was calculated by using the Wilrich POD LOD calculation software program (version 9, dated 23 September 2017) as in  [26, 30, 31]. (www. wiwiss. fu-berlin. de/fachbereich/vwl/iso/ehemalige/wilrich/index. html (accessed on 23 September 2017)).
ISO 15216 offers validated methods for the detection and quantification of HAV and norovirus in seven food matrices, such as Pacific oysters (Crassostrea gigas) and common mussels (Mytilus edulis)  [37]. However, to our knowledge, there are no data reporting the characterization of a method for detecting enteric viruses from fishes and fish meals. The experimental design from ISO 16140-4 was applied in this study for characterization of the method used to detect viruses in fish products. Another study including fresh or frozen seafood should be performed to assess the developed method. Studying the influence of the operator and of virus inoculation levels on virus recoveries improves the evaluation of method performance applied to fish samples. Analyzing a high number of samples, and taking into account different food processing factors and different matrix conditions (fat or presence of inhibitors), provides information on the variability of virus recoveries under different measurement conditions.
Acknowledgments: We would like to thank  P. Pothier  and  A. de Rougemont  ( French National Reference Center for Gastroenteritis Viruses, Dijon, France ) for providing contaminated stool samples. We are also grateful to  N. Pavio  ( ANSES, Animal Health Laboratory, Unité Mixte de Recherche (UMR) 1161 Virology ,  Institut National de la Recherche Agronomique (INRA), École Nationale Vétérinaire d'Alfort (ENVA), Maisons-Alfort, France)  for providing HEV-contaminated pig feces.
Data collection Data was gathered through qualitative semi-structured interviews. The interviews were held at each participant's own house or farm, in October 2020 and March 2021, or by phone in March 2021 (5 farmers). The interviews lasted between 50 min and 2½ hours. They were recorded by dictaphone with the permission of the participants, who signed informed consent. Names and places were anonymised as per social sciences deontology. The interview guide was structured in four parts. The first part aimed at gathering details on the farmer, the farm, and the flock; and on the farmer's motivations and values concerning their husbandry activity. The second part dealt with perceptions and practices for herd health management and gastrointestinal parasite control. The third part was about the farmer's professional environment and how they relate to it.
Data analysis The second author transcribed the first seventeen interviews in full. The other eight interviews were partly transcribed, focusing on particular moments in the interviews that were relevant for our study. Data were analysed using the qualitative data analysis software Nvivo 12 Plus (Lumivero, Denver, the United States). In Nvivo, we analysed the transcripts through thematic analysis  (Braun and Clarke, 2008). First, we set the wider themes for coding the interviews according to the interview sections. This methodological choice was intended to focus on our research topic while also allowing us to grasp the rich material covered in the interviews. We then iteratively built up new and more precise codes through successive readings of the transcripts. For example, we created codes to classify farmers' practices according to their use of preventive treatment, coprology, and alternative treatments.
The pH kinetics were analysed over a subsequent 7-day period. Relative pH indicators were used to classify cows with or without SARA. Milk, blood, faeces, and urine were collected for analysis of the indicators included in the models developed by  Villot et al. (2020)  on INRAE data that were externally evaluated using FARM data. Then, new models based on the same indicators were developed combining INRAE and FARM data to test whether a possible loss in performance was due to a limited validity domain of model by  Villot et al (2020). Finally, the models developed combining INRAE and FARM data were adapted to the on-farm application and enriched by increasing indicators from 2 to 5 per model using linear discriminant analysis and leave-one-out cross-validation. The sensitivities (truepositive rate) in external evaluation on FARM data were substantially lower than those from crossvalidation by Villot et al.
(2020) (range: 0. 1-0. 75 vs 0. 79-0. 96, respectively), and the specificities (true-negative rate) showed a larger range with lower minimum values (range: 0. 18-1. 0 vs 0. 62-0. 97, respectively). The sensitivities of new models developed combining INRAE and FARM data ranged from 0. 63 to 0. 77. Models involving blood cholesterol, b-hydroxybutyrate, haptoglobin, milk and blood urea, and models involving milk fat/protein ratio, dietary starch proportion, and milk fatty acids had the highest performances, whereas models including sieved faecal residues and urine pH had the lowest. Enriching models to three indicators per model improved sensitivity and specificity, but the inclusion of more indicators was less or not effective. Larger field trials are required to validate our results and to increase variability and validity domain of models.
5 to 100%, depending on the model). The best-performing models (precision > 90%) included variables from blood (bicarbonate and BHBA), milk (milk urea, FPR), and particularly FA composition (n-6 FA and C18:1tran-s10/C18:1trans11 ratio) and behaviour (DM intake (DMI) and the number of drinking acts)  (Villot et al. , 2020). However, the models by  Villot et al. (2020)  were developed using data from SARA induced experimentally with a unique and specific diet. A wide range of diets with a high risk of SARA are used on-farm and individuals fed the same diet can differ in SARA susceptibility, due to metabolic specificity or feeding behaviour and social hierarchy  (Khiaosa-ard et al. , 2018). We hypothesised that the models developed by  Villot et al. (2020)  have a lower discrimination capacity when applied on-farm probably because the calibration dataset (controlled experiment) and the evaluation dataset (on-farm) are heterogeneous, as the on-farm data would probably be out of the validity domain of the models by  Villot et al.
Animals, diets and experimental procedure The present study was carried out using data from one controlled experiment (INRAE) and one trial on commercial farms (FARM) carried out between 2016 and 2017 in which, dairy cows were housed in free stall barns and had free access to water. The first experiment (INRAE; described in detail by  Villot et al. , 2018; 2020)  was conducted at the dairy cow research facilities of the farm of Herbipôle (INRAE, Theix, France, https://doi. org/10. 15454/1. 5572318050509348E12). Eleven primiparous Holstein dairy cows (mean ± SE: 135 ± 7 days in milk; 27. 5 ± 2. 3 kg/day milk yield at the beginning of the experiment) were fed successively a low starch diet (13% of diet DM) or a high starch diet (35% of diet DM) as described by  Villot et al. (2018 and 2020).
Kinetics of reticulo-rumen parameters and drinking acts Reticulo-rumen (RR) pH and temperature were monitored continuously throughout both INRAE and FARM studies using a wireless sensor (eCow, Exeter, UK), as described by  Villot et al. (2018). The sensors were calibrated by heating in a water bath at 39 °C and calibrating against pH 4 and 7 standard buffers before insertion. Calibration was checked overnight using a pH 6. 86 buffer. Each RR sensor was set up to record mean pH over 15 min. The RR sensor was orally administered to the animals on sampling day, just after sampling of blood, faeces and urine, before the total mixed ration morning feeding. The pH kinetics were analysed over the following seven consecutive days, and daily relative RR pH indicators were calculated. Signal processing was applied to raw pH values in order to calculate relative pH indicators (NpH) by filtering and normalising data to remove inter-individual variability, sensor drift and sensor noise  (Villot et al.
Statistical analysis A total of 79 variables were obtained from seven different matrices (diet, behaviour, rumen, faeces, blood, milk, and urine) and then statistically treated using SAS  (Version 9. 4; SAS Institute, 2009). When necessary, data were log-transformed before analysis (details given in Supplementary Table  1 ). The main steps of our work with the related statistical procedures are summarised in Fig. 1. The SAS code of the statistical procedure applied is reported in the Supplementary Material 1. Step 1: Evaluation of models developed by  Villot et al. (2020)  using FARM data The models by  Villot et al. (2020)  are detailed in the Supplementary Table  1. An external evaluation of the multiple logit functions models published by  Villot et al. (2020)  was performed with Excel (Microsoft Office 365 Ò , 2013) by applying them to the FARM data only to predict the probability (y) of a cow to be affected by SARA (y > 0.
5). Models 1, 3, 6, 8 and 17 by Villot et al. (2020 including DMI as an indicator were not tested as it was not possible to measure DMI on-farm. Step 2: Development of new models using combined INRAE and FARM data, and based on indicators from  Villot et al. (2020)  In an attempt to understand whether a possible lower discrimination capacity was due to (i) non-robust indicators included in the models, or (ii) heterogeneity in datasets because FARM data would be out of the validity domain of the models by  Villot et al. (2020) , a principle component analysis (''proc princomp" of SAS) was first performed on the combined controlled INRAE and FARM data. The principle component analysis was based on the indicators used for the external evaluation of the models by  Villot et al. (2020). Hotelling's T 2 test was performed during the principle component analysis, selecting a 95% confidence region.
(2020)  and the calibration and cross-validation of each new linear discriminant model were estimated using a bootstrap resampling procedure (''proc surveyselect" of SAS). In particular, a random resampling with substitution (''outhits method = urs" of SAS) of a sample of the same extent as the original dataset was generated 20-fold. For each subsampled dataset, the external evaluation or calibration and cross-validation procedure was iterated and the average sensitivity, specificity, precision and accuracy and their SE were calculated  (Efron and Tibshirani, 1993). For each step, the target for each model was to reach values > 85% for both sensitivity and specificity, considered a reasonable threshold to allow a possible careful use for individual screening on farm  (De Marchi et al. , 2014; Villot et al.
Results Reticulo-rumen parameters and rumen, faeces, blood, milk, urine, behaviour, and diet indicators Descriptive statistics for rumen, faeces, blood, milk, urine, behaviour, and diet indicators from combined INRAE and FARM data are presented in Supplementary Table  2. The NpH times spent below < À0. 3 and below < À0. 5 ranged from 0 to 539 min and from 0 to 285 min, respectively. The NpH range varied from 0. 3 to 1. 12 and the NpH SD from 0. 09 to 0. 39. Forage/concentrate ratio ranged from 80/20 to 46/54 and starch proportion from 13 to 35% of diet DM. Variability in rumen NpH and in cow diet was reflected by a large variability in most of the blood, milk, faeces and behaviour indicators. For instance, milk FPR ranged from 0. 49 to 2. 15, urea from 1. 20 to 7. 16 mmol/L and SFA from 47.
1 Se = sensitivity; Sp = specificity; Pr = precision; Ac = accuracy; Mean from bootstrap procedure ± SE. 2 Variable added to the corresponding model presented in Table  5. When evaluating on-FARM data, the models built by  Villot et al. (2020)  in experimentally induced SARA, the discrimination capacity of the models decreased, meaning that their robustness was low. This low robustness can be due to a lack of homogeneity between the calibration and external evaluation datasets  (Shenk and Westerhaus, 1995) , as shown by their segregation in principle component analysis (Fig. 1 ), because of the FARM data were out of the validity domain of the models by  Villot et al (2020). In the research by  Villot et al. (2020) , SARA was experimentally induced by a specific diet, and the SARA+ and SARAÀ groups were fed two contrasted diets (different forage/concentrate ratio, maize silage vs.
grass silage, etc. ). Such contrasting diets may have increased the amplitude of differences for some indicators (i. e. milk FA composition; faeces physical appearance/sieving, behaviour, etc. Cabiddu et al. , 2022; Abdela, 2016; Humer et al. , 2018). Occurrence of on-farm SARA in cows fed the same diet depends on the individual susceptibility of the cows  (Khiaosa-ard et al. , 2018). Furthermore, data collected on-farm usually cover intermediate situations to which the models developed in controlled trials are not usually confronted to. This means that the dataset on which the models of  Villot et al. (2020)  were calibrated for a specific range of response could be different from those from commercial farms. Even if the indicators used by  Villot et al. (2020)  were fully biologically justified for the detection of SARA, another hypothesis to explain the low robustness of their models could be that the contrasting dietary conditions used in the experiment to induce SARA might have generated significant differences in the indicators, not primarily due to the occurrence of SARA.
Some of the indicators in the models of  Villot et al. (2020)  might have been more sensitive to diet change than to SARA, like some FA (i. e. SFA, MUFA; PUFA, n-6 FA), which are greatly sensitive to cow feeding  (Cabiddu et al. , 2022). Discrimination models developed on the same indicators combining INRAE and FARM datasets gave both sensitivity and specificity superior to 70%. These results, as well as the principle component analysis (Fig. 2 ), seem to confirm our hypothesis that the low robustness could be due to heterogeneous calibration and external evaluation datasets, as these last data were out of the validity domain of the models by  Villot et al. (2020).
However, at present, care in the application of our model for individual diagnosis of SARA is recommended as none of the models reached value >85% for both specificity and sensitivity, which can be considered a reasonable threshold to allow a possible use for on-farm screening  (De Marchi et al. , 2014; Villot et al. , 2020; Coppa et al. , 2021). A careful interpretation of the predicted results (i. e. by cross-checking the general health status of the animal) is also recommended since the indicators we used might also be sensitive to other diseases or to alteration in animal status  (Abdela, 2016). Improvement of model reliability would probably be possible by enlarging the dataset, with more cows monitored on several different farms (for both NpH and indicators), with a view to increasing the variability of cow characteristics (i.
Therefore, enlargement of the datasets with more farms and cows i to develop the models, in order to increase variability and validity domain, is recommended to validate our results and further increase models performance in discriminating SARA. This would be probably preferable to adding further indicators to the models. Further research is needed to confirm this hypothesis. In addition, the use of models discriminating situations at risk of health problems is essential to reduce the number of experimental animals in line with the objective of the 3R approach (Refine, Reduce, Replace experimental animals;  Vessier et al. , 2021).
Therefore, the aims of this project were (1) to study the correlation between the activity measured continuously by accelerometers and the one estimated by video analysis over short periods, and (2) to determine whether 24 h of activity could be predicted from these video data. The data set contained individual activity measurements, from accelerometers (recorded continuously) and video analysis (two periods of 5 h per day), taken over 24 days from 38 group-housed gestating sows. Statistical analyses were performed using a generalized linear model considering the observer of the videos (5), the sow's group (4), and the hierarchical rank. Prediction of accelerometer activity using data from the video analysis explained 23-64% of the variability for the two 5 h periods per day, and 39-46% for a combined period of 10 h (P < 0.
Preliminary interviews In order to understand the decision-making context of Corsican pig farmers, 4 exploratory semi-structured interviews were conducted with free-range pig farmers. The farmers were chosen to represent different farm categories de ned in previous studies (28). The preliminary interviews were conducted in 4 steps: (1) collection of general data on the farm in order to describe the sample and understand the context in which the farmer was operating; (2) open-ended questions focusing on the perception of health hazards and biosecurity measures, knowledge on biosecurity principles and measures, associated constraints and advantages, and reasons for implementing biosecurity measures or not; (3) open-ended questions on the future of the pig farming sector, and potential impact of biosecurity measures or of the introduction of ASF on this anticipated evolution; (4) a question on the likely response of the participant to the effective implementation of the regional porcine sanitary plan (RPSP).
Data analysis The quantitative data were analyzed using R software version 3. 6. 1  (33). Data from the ABSC were analyzed with a conditional logistic model using the "survival" package  (35). A likelihood ratio test as well as a Wald test were used to assess the quality of the model. PP results were analyzed by descriptive statistics. In order to take into account the direction of the individual preferences of the respondents towards the different attribute levels, individual results of PP for assessing the attributes' effects on the respondents' decisions were weighted by a -1 or +1 coe cient (positive or negative) depending on the attribute level chosen by the respondent for his "ideal" PSPR: the "soft" levels (least constraining or least costly for the farmers) were counted positively while the "strict" levels were counted negatively.
The study included healthy adult pets aged one year or older. Only data from the first visit of pets who presented multiple times were considered for analysis. During the consultation, the veterinarian assessed each pet's body condition score with the nine-points scale BCS and weighed the animal  [20]. All veterinarians involved in this study underwent comprehensive training and instruction to guarantee consistency in the evaluation process. They underwent a full day of training in each veterinary school consisting in scoring the same 30 dogs and 30 cats, ultimately reaching a consensus on the body condition scores. In the survey questionnaire, owners were first asked to evaluate their pets' body condition using the response options of "very skinny", "a bit skinny", "ideal", "a bit fat", or "very fat" in French.
We compared the behaviours of rabbits wearing a cloak to the behaviour of rabbits that were not fitted with a cloak. To do this, we used nineteen percent of the total number of videos. The videos were chosen randomly, at different ages, periods of the day and place of observation. Four rabbits (two with a cloak and two without a cloak) were followed for 15 min and their behaviours were recorded. Video recordings were analyzed using Boris software v. 7. 9. 24  (Friard and Gamba, 2016). We were able to collect data from 95% of the dressed rabbits. The following behaviours described by  Gunn and Morton (1995)  and  Coda et al. (2020)  were recorded (see Supplementary Table  S1 ): moving (Hopping and Walking), maintenance (Grazing, Eating pellets, Eating hay, Drinking, Resting, Gnawing), comfort (Stretching, Yawning, Grooming), exploration and alertness  (Pawscraping, Rearing, Sniffing, Stamping, Chinning, Watching) , and interaction (between two rabbits or more: Allogrooming, Side-to-side, High-speed chase, Nose-to-nose) and Capering.