dataesr/scientific-paragraphs-categorization huggingface dataset:
compatible), probably in relation to similarity between snail haemocytes and the surface carbohydrates of parasites  [1]. Given this, trematodes, which may be present in the space between the shell and soft body at the cercarial stage when leaving the first intermediate host or reaching the second one, are a good target to determine whether shell-encapsulation is taxon-specific. The helicids Cornu aspersum, Cepaea hortensis and C. nemoralis are some of the most studied snail species in Europe, and can be infected by various metazoan parasites such as nematodes, trematodes and mites (e. g. [3, 9, 15, 27] ). In this study, we used data from a range of local populations of these three species to determine whether they were able to use their shell as a defence system to encase and kill metazoan parasites other than nematodes, and especially trematodes.
Treatments, experimental design, goats, and management Two treatments were compared: "water access", with permanent access to drinking water (at pasture and in the barn), and "water restriction", with access to drinking water only in the barn. The water restriction occurred every day only during the grazing period (i. e. ca. 8 h/d, from 08:00 h-16:00 h). Based on their pre-experiment characteristics, measured from 23 to 30 May 2017, the 24 multiparous Alpine goats used in the study were divided into 2 homogeneous groups of 12 goats for the duration of the study. During this pre-experiment period, all goats grazed in a single herd, stayed in the same barn overnight, and had free access to drinking water at pasture and in the barn. The following mean data (±1 SD) were recorded: lactation number (3.
6 ± 1. 64 lactations), stage of lactation (96 ± 9. 9 days in milk), milk production (3. 5 ± 0. 37 kg/d), milk fat concentration (34. 2 ± 2. 32 g/kg), milk protein concentration (29. 5 ± 2. 48 g/kg), and body weight (52. 7 ± 5. 55 kg). The difference between group means did not exceed 3% for any of these variables. The experimental design was a crossover design, in which each group of goats was allocated to one treatment for period 1 (14 d) and switched to the other treatment for period 2 (14 d). The first week of each period was used to acclimate the goats to the treatments, and only data collected during the second week of each period were used to determine the effects of water restriction at grazing by statistical analyses. Goats were milked twice daily, at 07:00 h and 16:30 h. After the evening milking, the two groups were kept in a large barn and managed separately, each in a 4 m × 7 m pen that had a bedding area of 28 m 2 (i.
Samples were combined per treatment and week, and ground through a 0. 8 mm screen before chemical analyses. Organic matter, nitrogen, NDF, ADF, and ADL concentrations were determined as described by  Charpentier et al. (2019a). Weather conditions (i. e. air temperature, air humidity, rainfall, wind speed) were measured hourly at an INRAE weather station 500 m from the experimental paddock. Data were monitored from the INRAE CLI-MATIK platform (https://intranet. inrae. fr/climatik/, in French) managed by the AgroClim laboratory in Avignon, France. Downloaded data were averaged per day, then per week and measurement period. The temperature-humidity index (THI), an indicator of the severity of heat stress, was calculated daily using the equations of  West (1994)  and  Tucker et al. (2008). As both equations yielded the same results (THI_-Tucker = 0.
Calculations and statistical analyses Daily milk production, milk composition, and grazing behaviour data, measured on several days and individually during the second week of each period, were first averaged per goat. Regarding behaviour, in addition to daily grazing time, hourly grazing time from the start of the grazing session in the morning was calculated, then also averaged per goat and period for each hour of the day, to describe the daily pattern of grazing activity. The goat × period data were the statistical unit. All data were then analysed by analysis of variance (ANOVA) as a crossover design according to the following model (PROC MIXED; SAS Institute, 2008): Y ijk = μ + Treatment i + Period j + Goat k + e ijk Model 1 Where Y ijk represents the variable analysed, μ represents the overall mean, Treatment i (n = 2) represents the fixed effect of the treatment, Period j (n = 2) represents the fixed effect of the period, Goat k (n = 24) represents the random effect of the goat, and e ijk represents the residual error term.
Daily drinking water intake in the barn and total (at pasture plus in the barn) drinking water intake, measured at the herd level during the second week of each period (along with milk and behaviour data), were first calculated per goat per day by dividing herd data by 12, and then analysed by ANOVA (PROC GLM; SAS Institute, 2008) using the following model: Y ijk = μ + Treatment i + Period j + Day(Period) k + e ijk Model 2 Where Day (Period) k (n = 14) represents the fixed effect of the day within a period. In addition to the main treatment effects analysed, the between-day and between-treatment difference in total drinking water intake during the study was related to daily weather conditions (daily mean Ta or THI) by covariance analysis and linear regression using the following models (PROC GLM; SAS Institute, 2008): Y ij = μ + Treatment i + Covariate + (Treatment i × Covariate) + e ij Model 3 Y i = μ + Covariate + e i Model 4 Where Y ij and Y i represent the total drinking water intake, Covariate represents the weather variable (Ta or THI), Treatment × Covariate represents the interaction between the treatment and the weather variable, and e ij and e i represent the residual error term.
The covariance analysis was performed using all available data (n = 26 days, due to 2 days missing at the start of the study), including the acclimation weeks, to increase the range of weather conditions. Pasture chemical composition, pasture characteristics, and grazing management data, averaged per period and treatment when measurements were repeated on several days, were analysed as a crossover design using the following model (PROC GLM; SAS Institute, 2008): Y ij = μ + Treatment i + Period j + e ij Model 5 Where Y ij represents the variable analysed. The threshold for a significant treatment effect and for a trend was set at P ≤ 0. 05 and ≤ 0. 10, respectively.
Assuming that goats in both treatments ingested approximately 1. 5 kg DM/d from pasture fresh matter under the grazing and supplementation conditions of the present   study  (Charpentier et al. , 2019a; 2019b) , they ingested approximately 6. 9 kg of fresh herbage per day, of which 5. 4 kg was water. During grazing, goats with water access thus likely ingested approximately 7. 3 L of water (5. 4 L from herbage + 1. 9 L drunk), vs. 5. 4 L for water-restricted goats, which is only 26% less compared to goats with water access at pasture. Average total drinking water intake in the present study (5. 6 L/d) was similar to a reported mean of a large dataset (5. 7 L/d; Giger-  Reverdin et al. , 2011)  for Alpine or Saanen dairy goats fed diets based on hay or maize silage, with mean water intake from feed estimated as 2.
Acknowledgements The authors thank  J. Lassalas ,  P. Lamberton ,  E. Siroux ,  F. Briot , and  A. Eveno  ( PEGASE ,  35750 Le Rheu, France ) for milking, feeding, and caring for the goats, and for their help during the measurement periods, as well as  T. Le Mouel ,  N. Huchet , and  S. Giboulot  ( PEGASE ,  INRAE-Institut Agro ,  35590 Saint-Gilles, France ) for the laboratory chemical analyses. This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
In 2020, Miranda de la Lama and colleagues proposed a shorter checklist and a subsequential categorization of the horses in four clusters based on four animal-based indicators (body condition score, coat quality, prevalence of lameness, and ocular and nasal discharge) assessed on arrival at two slaughterhouses in Mexico  [30]. However, the study does not report any calculation to assess the overall welfare index of the horses  [30]. Considering the knowledge gaps described above, this study aimed to develop a tool to determine an overall welfare score for horses at unloading and to create a scale (GLAD scale) for the categorization of the horses into different welfare states. As an illustrative application and working example of the approach, data collected from over a thousand horses transported to slaughterhouses were used.
Data Collection Data collection was performed from May 2021 to May 2022 for horses traveling on 52 commercial journeys. A total of 1019 horses transported from different countries to three different Italian slaughterhouses, 2 in southern and 1 in northern Italy, were used. On arrival at slaughterhouses, the welfare status of the horses was assessed following the protocol described by Zappaterra et al. [29]  (modified from Messori et al. [27] ). Briefly, the Animals 2023, 13, 1465 3 of 16 assessment was performed 3 times, filling in 3 specific checklists during unloading, 30 min after, and 24 h after unloading. The checklists included animal-based measures (ABMs) and environmental-based measures (EBMs) recorded at 3 levels: driver, vehicle, and animal.
The journey conditions enforced by this regulation seem to reduce the risk of downer animals. By applying the GLAD scale to the dataset of this study, it was found that the majority of the horses fell into the G and L categories. These results indicate that the majority of horses were in a generally good state, did not need any veterinary attention, and could be slaughtered immediately or shortly after arrival. This supports previous studies undertaken using these types of datasets with horses  [28, 29] , namely that they arrived in overall good health and welfare conditions. Nonetheless, our results are in contrast with what was reported by Marlin et al. [36] , where 37% of horses that arrived at the slaughterhouse after a long-distance journey were deemed unfit for transport. The horses in our study travelled for long journeys too, but, despite this, arrived with a good welfare status.
Nevertheless, it still has some limitations. First, the calculation of this study is based on a dataset where the ABMs were scored by a trained and expert assessor, but untrained assessors may misjudge the ABMs, and, consequently, the welfare impairment of the horses. A further study will be conducted in which several sets of horses will be evaluated by different assessors to capture both the inter-and intra-variability and repeatability of the ABM scoring. Second, in a top-down approach, we used a priori thresholds to classify the horses in GLAD. In a bottom-up approach, the work would consist of using empirical data on the dispatching of horses on arrival at the slaughterhouse to build the thresholds and outline a validation. Third, the entire study is based on a population of horses exclusively destined for Italian slaughterhouses.
Acknowledgments: The authors are grateful to  Rebecca Boulert  for the English editing of the paper and to  Chiara Zangoli  for the help in data entry. The authors also thank the owners of the slaughterhouse and the drivers who gave consent to take part in this study. In particular, the authors thank  Martina Zappaterra ,  Pietro di Pinto ,  Francesco Perniola , and  Daniele Tullio  for the help in data collection, as well as their commitment to improve animal welfare.
To extract appearance information, a common solution is to use a convolutional neural network (CNN), trained on a large dataset before running the tracking algorithm. For farmed animals, designing such network is challenging as far as large training dataset are still lacking. In this article, we proposed an innovative solution, where the CNN used to extract appearance information is parameterized using offline unsupervised training. The algorithm, named Wizard, was evaluated for the purpose of goats monitoring in outdoor conditions. 17 annotated videos were used, for a total of 4H30, with various number of animals on the video (from 3 to 8) and different level of color differences between animals. First, the ability of the algorithm to track the detected animals was evaluated. When animals were detected, the algorithm found the correct animal ID in 94.
On the contrary, the third type of methods are off-line algorithms that solve the problem of identification at the video scale and not at a frame by frame scale (see for example  Braso and Leal-Taixe, 2020). Many innovations were proposed for MOT by computer scientists, but mostly for tracking humans, where large public dataset are available. However, there is generally a gap between the published results of the state-of-the-art MOT methods and the one obtained when tracking farmed animals in an original setup. Some particularity of the farmed animals data are: public dataset are scarced, occlusions are frequent, animals can exit and re-enter the field of view, animal movements is generally non linear and animals can have very similar appearances. But also because the animal tracking community remains small.
Second, each movie was entirely reviewed and manual annotation were required when: (i) the Yolo detections were too different from the reality or (ii) during an occlusion sequence. The first case, for example, happened when two animals were included inside the same bbox. The second case was easily detected as no bbox were displayed for the hidden animals. To correct and add labels and bbox, we used the video labeler app from Matlab or the CVAT application. All the videos were thus used to evaluate the tracking algorithms, which represents 4H23m52s of videos, 368,244 frames, and 1,615,589 labeled bbox.
General idea of the proposed method The algorithm Wizard can be viewed as an extension of Deep-Sort  (Wojke et al. , 2017a) , that is, using appearance, motion and location for animal ID assignment. The main difference between our approach stands on the CNN used to extract appearance information of each animal. With DeepSort, this CNN has to be pre-trained before running the tracking method. On common examples, training is made on large publicly available dataset, generally with common objects such that cars or humans. In livestock farming, this type of dataset are still missing, and the situation might not change quickly. In this work, we propose to train the CNN that extracts the texture features (TF), or appearance information, online, directly on the frames from the video. But also to consider the CNN-TF as a classification CNN, with one class for each animal of the video.
In this study, we used Yolo v7-X, which contains 71. 3 parameters, and is claimed to have the optimal tradeoff between performance and accuracy  (Wang et al. , 2022a). We used a dataset of 2,530 images mainly coming from previous personal studies, with various cameras and field of view. Only 185 images were taken from the present study on the Gardel site. In total 2,303 images were used for training and 227 for validation. The model has been learned with 150 epochs and the default hyper-parameters 6  (Wang et al. , 2022a). On the validation set, precision and recall was equal respectively to 91. 77% and 83. 89%. Note that we also trained a Yolo v5  (Jocher)  for comparison. In this case, precision was equal to 87. 06% and recall to 76. 73%, confirming the improvement made by this new version of Yolo.
An SGD optimizer, through 150 epochs was used to optimize the network parameters. The learning rate was fixed to 0. 05 and momentum to 0. 9. Finally a ReduceLROnPlateau scheduler was used to slightly decrease the learning rate, parameterized with _ = 0. 005,  = 10 and   = 0. 4. Data augmentation technics, detailed in Section 3. 7 was also used and applied during the network pre-training. This initialization step was done 3 times, thus a total of 150 × 3 = 450 epochs were done, with a random dataset extraction and split for each iteration.
Wizard validation For the validation of Wizard, we used two different methods. The first method is common to evaluate tracking algorithm in the case of farmed animals and consists in estimating the time percentage of good tracking for each video. Second, Wizard was compared to three different tracking algorithms, named DeepSort  (Wojke et al. , 2017b) , BoT-SORT  (Aharon et al. , 2022)  and ByteTrack  (Zhang et al. , 2022). We used a PyTorch implementation of the original DeepSort algorithm  (Wojke et al. , 2017b) , available on github. We used the Ultralytics implementation of Bot-SORT and ByteTrack  (Jocher et al. , 2023). Bot-SORT is a tracking-by-detection algorithm similar to DeepSort. It relies on an improved version of the Kalman filter algorithm, a camera motion compensation based on sparse optical flow, and a particular CNN-TF.
e. (   *  -   ) > 0, and in some sequences of the Wizard track, the distance with the corresponding animal in GT could be large, which was the case when Wizard mistakenly re-identify an animal. Second, to identify these sequences, we performed a frame by frame analysis between the track from Wizard and from GT. We simply computed the union bbox overlapping ratio between the bbox of Wizard and GT tracks for the same animal ID. When the ratio was equal to 0, we considered that the animal track was lost and that Wizard was assigning the ID to the wrong animal. The PGT is then simply the number of frames for which the ID from Wizard correspond to the same animal ID in the GT data (i. e. overlapping ratio > 0), divided by the number of frames of the Wizard track. The PGTD is the number of frames for which the ID from Wizard correspond to the same animal ID in the GT data, divided by the number of frames where the animal was present in the ground truth.
Discussion We proposed an algorithm, named Wizard, ables to track a known number of farmed animals on videos. The algorithm was evaluated for goats tracking on 17 videos, with a number of animals varying from 3 to 8, with different level of color differences between animals. Wizard, as many other tracking algorithms, used location and appearance information to associate detections between frames. The novelty of the algorithm comes from the fact that the CNN used to extract appearance information, is trained online and the training is unsupervised. This is particularly interesting for farmed animals monitoring where large public dataset are still lacking. We found no statistical evidence that the number of overlaps, the level of color difference between animals and the number of animals had impact on the percentage of good tracking.
Acknowledgments We would like to thank  Rémy Arquet ,  Nathalie Minatchy ,  Dimitri Romil-Granville  and all the members of  PTEA  staff for animal care and help in designing the experimental setup. Parts of he computations were performed using Exocet, the  HPC server of the Centre Commun de Calcul Intensif of the Université des Antilles (FWI).
Observers and training Three observers (Obs1, Obs2 and Obs3), blinded to cow treatment, scored the 156 videos of video set 1. Obs1 (man) was a technician in animal behavioral science with 22 years of experience in video scoring, and Obs2 and Obs3 (women) were two final-year veterinary students specialized in cattle medicine. Before starting video coding, the observers followed a training program covering acquisition of each FAU (Obs1, 2 and 3) (Table  1 ), familiarization with the scoring software (The Observer XT 14, Noldius, Wageningen, The Netherlands) (Obs2 and Obs3), and a practice scoring session (Obs1, 2 and 3).
Selectivity of FAUs to assess pain 2. 9. 2. 1. FAU's evaluation after LPS infusion: construct validity. To evaluate changes in each FAU after LPS infusion in the udder, we used the videos from LPS cows scored by Obs1. Each cow was her own control. To eliminate the circadian rhythm effect on cows' behavior  (Veissier et al. , 2017) , we compared changes in each FAU at the same hour of the day before vs. after challenge (i. e. 11:30 h: T-21 vs. T3, 13:30 h: T-19 vs. T5, 16:30 h: T-16 vs. T8). Data were graphically checked for normality. Because data did not follow a normal distribution, duration or frequency of each FAU before and after challenge were compared using a Wilcoxon rank sum test. Proportion of LPS cows assigned to a 'presence' or 'absence' modality before vs. after challenge was compared using a McNemar test (Table  2 ).
Definition of the pain threshold for each FAU To define the pain thresholds for each FAU considered as selective (through construct validity) (see. 2. 8. 1. 1), we used the behavioral data from the videos of LPS cows scored by Obs1. First, the data of duration and frequency were rescaled (by dividing each value by the maximal were assigned to the negative modality (non painful) and all the data point after challenge (T3 = 11:30 h, T5 = 13:30 h, and T8 = 16:30 h) were assigned to the positive modality (painful). Second, the pain threshold and area under the curve (AUC) were determined for each FAU at each time point studied (i. e. T-21 vs. T3 = 11:30 h, T-19 vs. T5 = 13:30 h and T-16 vs. T8 = 16:30 h) using ROC curves and Youden index methods  (Swets, 1988; Delacour et al. , 2005; Dohoo et al.
Intra-observer reliability of FAUs: repeatability To assess intra-observer reliability, each observer (Obs1, Obs2 and Obs3) scored 21 randomly selected videos (see 2. 6 for details). Each observer scored three times each of the 21 videos. Then, in order to obtain binary data, the pain thresholds determined in the previous step (see 2. 8. 2) at each moment of the day (i. e. thresholds of 11:30 h, 13:30 h and 16:30 h) were applied on continuous data (duration or frequency of the FAUs observed) scored by observers whatever the time point videoed. Therefore, (i) when the three pain thresholds were available, they were applied on each of the 21 videos, creating a total set of 63 observations (21 videos × 3 pain thresholds) per FAU per observer; (ii) when only two pain thresholds were available, both were applied on each of the 21 videos, creating a total set of 42 observations (21 videos × 2 pain thresholds) per FAU per observer; and (iii) when only one pain threshold was available, it was applied on each of the 21 videos, creating a total set of 21 observations (21 videos × 1 pain threshold) per FAU per observer.
Inter-observer reliability of FAUs: reproducibility To assess the inter-observer reliability of all observers together, we used the final set of 156 videos from LPS cows and LPS+NSAID. Then, in order to obtain binary data, the pain thresholds determined in the previous step (see 2. 8. 2) at each moment of the day (i. e. thresholds of 11:30 h, 13:30 h and 16:30 h) were applied on continuous data (duration or frequency of FAUs observed) scored by the observers for each corresponding time point (i. e. pain thresholds of 11:30 h were applied on the data from videos at 11:30 h, pain thresholds of 13:30 h were applied on the data from videos at 13:30 h, pain thresholds of 16:30 h.
e. total number of LPS cows observed after challenge) (Table  2 ). For each FAU at each time point, a cow was considered as 'detected in pain' or 'detected not in pain' based on the pain thresholds (determined in 2. 8. 2), and we considered that cows were actually 'not in pain' at T-21, T-19 and T-16 but 'in pain' at T3, T5 and T8. FAUs that presented a sensitivity ≥ 75% for at least one of the three moments of the day (i. e. 11:30 h, 13:30 h and 16:30 h) were considered as sufficiently sensitive (Fig. 3 ). All analyses were performed using R software version 3. 6. 2 (2019). The ROCR package  (Sing et al. , 2005)  was used to plot ROC curves, the AUC package was used to estimate AUCs, the irr package was used to estimate PA, and the DesTools package was used to estimate Fleiss' kappa coefficient and confidence interval (CI).