dataesr/scientific-paragraphs-categorization huggingface dataset:
Results To avoid results' fluctuation due to random sampling of the test set, the random sampling process of the database was repeated 50 times, and the average, minimum, and maximum of percentage error were estimated for each repetition, as well as the width of the Bland and Altman limit of agreement. Table  1  shows the mean results for each model. With an average percentage of error of 10. 9% against 51. 4% for the ODBA method, the SVM model clearly outperformed its competitor. The Bland and Altman plot of one SVM repetition is shown in Figure  6  (top), with one circle corresponding to one stride. The speed predicted by the model and the measured speed of the stride were compared. The mean bias was zero, which meant that on average, the SVM model output was close to the measured speed.
Precisely, the stride was detected on the Z-axis (dorso-ventral axis), but 101 points were kept from the maximal peak on the Z-axis. This change allowed keeping the stride duration information: for high speed, the "stride" would have a signal that contained more canter cycles than low speed "strides". This cutting helped increase the accuracy of the machine learning model. Indeed, the presented model applied to data sampled on real strides showed the average percentage of errors above 0. 6 m/s increase by 2%. To evaluate the presented SVM method, its results were compared with those of another method that did not need external inputs to estimate speed per stride from new IMU data: the ODBA method. In fact, direct signal integration methods, which are commonly used in the human case or in the horses running on a treadmill case, need strong assumptions to calculate an integration constant for speed estimation, which cannot be made when the horse moves in real conditions and when the IMU is not located on the limb  [5, 17].
Indeed, slow and extremely fast canter strides represent currently less than 20% of the database, which could explain the lower model performance for those situations. Despite its accuracy, our model presented some limits: we should validate the use of an automatic chronometer to estimate average speed in a future study, comparing it to the camera motion capture system on straight paths for example. Moreover, the presented model should not be directly transferred to a discipline other than show jumping because show jumping canter is specific to the discipline. For example, in endurance horses, flat canter is preferred, while in show jumping, the bounce is important in order to help the horse gather more vertical than horizontal energy to ease the jumps. Therefore, in order to adapt our tool to other disciplines, the model has to be expanded with more data gathered in new situations.
Acknowledgments: The authors thank those other members or guests of this and other  IPEMA Work Groups , who contributed directly or indirectly to the discussion and input for this review, namely  Johannes Baumgartner ,  Antoni Dalmau ,  Emma Fabrega ,  Sonya Ivanova ,  Kevin Kress ,  Christine Leeb ,  Nathalie Quiniou ,  Camille Raoult ,  Deborah Temple  and  Raffael Wesoly.
However, the use of tools (such as hooks) by the mahouts was not allowed during the task, meaning the elephants were not punished for refusing commands. Although a potentially restrictive context under which to evaluate behaviour, this task standardises the collection of behavioural data and allows comparisons to be easily made between animals, dates and locations. We used two novel objects: a water bottle in 2017, which represented a novel appearance, feel and sound when manipulated, and a green plastic disc in 2018, which represented a novel shape and feel. We also used small pieces of nearby timber as control objects as they were familiar to the elephants. In total, we recorded 235 single object pick up videos (118 known and 117 novel) across 118 individual task events, including repeated observations for 14 individuals that were tested in both years.
All tasks were carried out during the morning, when it was cooler, to minimise temperature-induced stress, in timber camps located in Katha and Kawlin, in the Sagaing Region of Myanmar. Behavioural research practices were approved by national governmental authorities and the ethical board of the University of Turku. To quantify the behaviour of elephants in response to the object pick up task, we compiled distinctive behaviours from the videos into an ethogram of elephant behaviour (Table  1 ). To measure the magnitude of overall agreement between observers when making observations using this ethogram (inter-observer reliability), we employed three volunteer biology students from the University of Sheffield (observers A, B and C). At the time of data collection, students were in their first or second year of undergraduate study, and though they will have been taught about general theories of animal behaviour and behavioural ecology, they had no prior experience in elephant behaviour research or specialist training in stress behaviour.
These observers coded the frequency of ethogram behaviours observed in the videos, using the Behavioural Observation Research Interactive Software (BORIS version 7  [43] ). To familiarise observers with the behaviours and video coding software, they first went through an initial training phase. This began with an introductory meeting involving a discussion of the ethogram (with example video clips) and a practise run-through of the coding software, after which the observers were asked to code 18 videos in their own time (questions via email were encouraged). This data was compared to observations made by the lead author, so that general feedback on their recognition of ethogram behaviours could be provided; for example, at what point to count smaller ear movements as an ear flap event. A second round of practise video coding was originally planned following this feedback, but observer performance was deemed sufficient to move directly onto the next stage.
Elephant Behaviour Repeatability We then assessed the repeatability of elephant behaviour between known and novel object types, from 109 known and 108 novel object tasks (with 10 elephants tested in both years; a further four elephants were omitted as their videos were used in the initial training phase). Models were fitted using the rptR package in R  [49] , which allowed us to calculate repeatability while also incorporating a generalised linear-mixed model structure. Adjusted R 2 values were generated for each behaviour at the level of the random effect of elephant identity, after accounting for the fixed effects of object type and observer identity, and uncertainty in fitted models was evaluated using 1000 bootstraps (link-approximated R 2 will be presented for generality). Distributions we used in repeatability models for each behaviour can be found in Table  S1  with testing time and holding time (Table  1b ) being rounded to the nearest integer so that a Poisson model could be fitted.
Results The ethogram constructed from object pick up tasks performed by 104 elephants included six general elephant behaviours found in zoo welfare assessment material  [28]  and the elephant gestures database (  [50] ; Table  1a ), and eight object-directed behaviours relevant to the working life of MTE elephants (Table  1b ). Because of the restricted (yet standardised) nature of the object pick up task, only a limited number of behaviours were incorporated into this ethogram, with behaviour involved in other contexts (such as feeding and sociality) being excluded. Of the general behaviours, trunk curl-drop is highly characteristic and most closely resembles a mixture of 'trunk-twisting' and 'trunk-curl' from the gestures database, however, descriptions sufficiently matching this behaviour could not be found.
Elephant Behaviour Repeatability When accounting for object type and observer identity, the repeatability of elephant behaviour varied between known and novel object tasks depending on which behaviour was assessed. For six behaviours, repeatability across object types was excellent, for one it was good, for five it was moderate and for the remaining two it was poor (Table  4 ; see Table  S2  for behaviour frequencies). Behaviours exhibiting high repeatability were potentially more closely associated with within-individual variation in behaviour than those with lower repeatability. For example, object pick up was highly repeatable (R 2 = 0. 953), suggesting the probability that an elephant picked up an object was highly associated with consistent individual behavioural patterns, irrelevant of the type of object stimulus and after accounting for differences in observational data between observers.
Discussion Behavioural observations are commonly utilised in the welfare assessment of captive Asian zoo elephants  [33] , but here we evaluated their reliability in the context of larger human-employed working populations, using a sizable dataset of 100 elephants. To assess the reliability of behavioural observations for elephant workers possessing various levels of behavioural knowledge, we tested observations carried out by non-specialist observers. From behavioural observations made on 217 videos of 100 elephants, our study has shown that with relatively rapid training, three observers with no prior experience in elephant research or management can reliably identify a series of behaviours relevant to the daily working-life and welfare of MTE elephants. By evaluating the objectivity of behavioural observations using two approaches of estimating reliability, we have shown that observers not only independently agreed on the frequency of all ethogram behaviours at a moderate-to-excellent level (ICC > 0.
Acknowledgments: We thank the  Ministry of Natural Resources and Environmental Conservation , the  Government of the Union of Myanmar , for giving us permission to work with the Myanma Timber Enterprise. We also thank  Khyne U Mar ,  Khin Than Win ,  Mu Mu Thein ,  Thu Zar Thwin  and  Ohnmar Win  for helping with data collection, and to all elephant handlers and MTE staff who participated in and helped with this project. Thanks to the three observers from the  University of Sheffield  for volunteering their time to carry out behavioural observations. Finally, we thank the two anonymous reviewers for their comments that helped to improve the overall quality of this manuscript.
Statistical Data Analysis and Proficiency Testing At least 3 independent runs executed on different days using cell cultures of different passage numbers were performed for cell growth rate determination (tier 1) and the MTS assay (tier 2 and 3). An initial analysis of the raw data was done by the individual laboratories using a spreadsheet calculation template enabling immediate evaluation of compliance with the test acceptance criteria mentioned in the SOPs, as well as automated data analysis and plotting (Supplementary Materials). Only data sets from fully acceptable tests were considered valid to be included in the final statistical analysis. In the determination of cell growth rate, the total cell counts and percentages of cell viability were calculated, based on the live and dead cell numbers.
Laboratories' Proficiency in Performing the In Vitro MTS Assay After Training (Tier 3) In the third and final tier of the study, six laboratories were enrolled, of which five also had participated in the first phase (tier 1 and 2) with involvement of the same operator, and four had not been able to provide a full set of valid data in the second tier. The other laboratories dropped out mainly because of a shift or lack of resources at the time of the last tier. The participating laboratories were provided with fresh FBS and NPs, and used the optimized MTS assay SOP. All laboratories sent in data on three independent, valid runs performed on cells of different passage number from the same working cell bank vial. The dose-response data were evaluated for intra-and inter-laboratory biases, similar to the statistical analyses in tier 2 (Figures  S5-S7 ).
In Table  3 , the mean ± SD values of EC30 and EC50 obtained by the six laboratories involved in the study before and after training are presented, together with the overall mean ± SD values of all laboratories. Table  3. Comparison of intra-and inter-laboratory biases for EC30 and EC50 values before (tier 2) and after (tier 3) training in quality aspects of MTS assay performance. Individual data from the six laboratories that were trained, as well as the overall data of all laboratories (N; in bold and between brackets) are included. Mean and SD (and SD*, in bold and between brackets) values of calculated EC30 (nM staurosporine) and EC50 (µg/mL PS-NH2), coefficient of variation (CV) (%) and number of runs (n) are given. For tier 2, data from all (valid and non-valid) runs and valid runs only are indicated.
analysis and proficiency testing, H. W. ; writing-original draft preparation, I. N. , A. H. (Andrea Haase) and A. S. ; writing-review and editing, I. N. , A. H. (Andrea Haase), S. A. , L. R. , A. J. , H. W. , C. R. , A. L. , J. -P. P. , O. T. , B. T. , G. L. , A. C. G. , S. C. , S. D. , C. W. , T. L. -F. , Ã. G. -F. , M. D. , A. H. (Anna Huk), V. S. , N. K. , M. N. , M. S. , S. M. (Stefania Meschini), M. G. A. , N. L. , M. R. , M. V. , F. B. , J. T. , T. B. , S. M. (Silvia Milani), J. R. , A. S. , K. A. D. ; visualization of results, I. N. , H. W. ; funding acquisition, K. A. D. All authors have read and agreed to the published version of the manuscript. Funding: This research received financial support from the EC FP7 project QualityNano (INFRA-2010-1. 1. 31-262163). The contribution of LIST was supported by the Fonds National de la Recherche of Luxembourg within the project NANION (FNR/12/SR/4009651).
Acknowledgments: The authors would like to thank  Siegfried Hofman  ( VITO ) for advice concerning statistical methods for proficiency testing of laboratories and  Christelle Saout  ( UNamur ) for expert discussions on SOP development. We are grateful to  Magda Marchetti  (ISS),  Pavel Rossner  (IEM),  Zuzana Novakova  (IEM),  Lisa Wollenberg  (BfR),  Dorit Mattern  (KIT),  Maximilian Kohnle  (KIT),  Kelly Blazy  (INERIS) and  Christelle Gamez  ( INERIS ) for their excellent technical assistance.
Statistical Analysis Daily activities and grazing cycles were analyzed with a repeated MIXED model on SAS 9. 4 software (SAS Institute Inc. , Cary, NC, USA). Group (Dam, Mixed or Standard), day (only 0, 1, or 7) and their interaction were included as fixed effects. The individual calf was considered as the subject of repetition, with day being the repeated factor. We used a compound symmetry covariance structure. Time to start grazing and herbage selection were analyzed with the same model, except that the day effect included all days. Average daily gain was analyzed with a similar repeated model, which included group, period (before or after pasture) and their interaction as fixed effects, calf as subject, and period as repeated factor. In this last model, the number of days since each calf had been weaned was used as a covariate.
FUNDING This study was part of the  EU -project  ProYoungStock  funded by  CORE Organic  Cofund funding bodies, which are partners of the  Horizon 2020 ERA-Net project CORE Organic Cofund. This research was financed by the  French government IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ). AN was supported by a doctoral fellowship from the  Fondazione Cariparo at University of Padova (Italy).
Population This work is the first national large-scale study on the risk factors of LBW in dogs. Seven of the ten canine breeds investigated belong to the top-twenty breeds born in France according to Société Centrale Canine (the French Kennel Club)  [17]. The breeders included in the current study were widely distributed around France (Additional file 1). Even though the information collected in this study represented a large sample, a selection bias could not be excluded as the data were obtained via convenience sampling. Data collection was challenging due to the individual approaches of each breeder. Indeed, no professional tools centralising puppy birth weight data are currently available to French breeders. Having an automatic data collection system would increase the efficiency of future studies.
Data collection The data used in this study were collected through a questionnaire (Additional file 2) sent out to French dog breeders from 2015 to 2019. The questionnaire was distributed using mailings, Facebook® messages, during canine exhibitions and via various dog breed associations  [6]. Completion of the questionnaire was voluntary. Recorded data included information about the litter (date of birth, breed), dam (identity and age at whelping) and puppy (sex, birth weight, mortality during the first three weeks of life). This information was then anonymously transferred to a Microsoft Excel table (Microsoft Corporation, Redmond, Washington, USA) for analysis. After data cleaning, all stillborn neonates, puppies with no birth weight provided or with unknown status regarding mortality and puppies from breeds with fewer than 70 individuals and/or with less than 20 deaths during the neonatal period, were excluded (Fig.
Statistical models Three binary logistic regressions, allowing two-by-two comparisons, were applied to identify factors associated with the likelihood of belonging to a given category (NBW, LBW or VLBW). For each model, puppies belonging to the two categories involved in the paired comparison were extracted from the full dataset and their category represented the outcome (NBW vs LBW; LBW vs VLBW; NBW vs VLBW). The candidate explanatory variables were: dam age at whelping, litter size (total number of puppies born alive), stillborns in the litter (absence or presence), puppy sex and breed. Factors related to the survival of LBW and VLBW puppies were analysed by constructing two binary logistic regressions. Puppies in the category under consideration (LBW or VLBW) were extracted from the full dataset and the binary variable (dead/alive) represented the outcome.
The candidate explanatory variables were dam age at whelping, litter size, stillborn in the litter, sex, breed and litter heterogeneity. For each model, preliminary bivariate analyses were applied to select candidate predictors (chi-square test of independence and Kruskall-Wallis rank sum test for the categorical and continuous variables, respectively). All candidate explanatory variables associated with the outcome with a P of 0. 1 or less in the univariable analysis were included in the corresponding multivariable logistic regression  [26]. The multivariable analyses were conducted by randomly dividing the selected subdataset into a training set and a testing set containing 70 and 30% of the data, respectively. The model was fitted on the training set and classification performances were evaluated on the testing set.
Description, variable selection and statistical analyses. A descriptive analysis of the different health and behavioural data made it possible to consider their levels of occurrence as well as their progression over time, leading to an initial sorting of the indicators according to their frequency. Those levels and their progression over time are reported in Supplementary Figure  S2. Three indicators related to very degraded states that could justify the elimination of ducks had a very low prevalence, that is, less than 2. 5% (Dead, Prostrate, Lame). Due to these very low values, we did not make statistical analyses on these variables. All the data were analysed with R software (R-core Team). The significance level was set at P < 0. 05. Quantitative data (body, carcass, liver, breast skin, breast and abdominal fat weights) were analysed using linear models, with the dietary strategy (two levels: MI or HI), the assessment stage (two levels: MID or END) and the interaction between those two factors as fixed effects.
Acknowledgements The authors are grateful for the advice and technical assistance of the staff of the  Palmipeds for Foie Gras Production Experimental Unit  ( INRA UEPFG, Benquet, France ). We thank  Marion Pertusa  for her help during the experiment and  Gail Wagman  and  Geoffrey Chiron  for the revision of the English language. This project received financial support from the  Future Investments Program  (action: Agriculture and agri-food programs of the future, P3A) managed by FranceAgrimer under Contract No. 2017 , and from  CIFOG. J. Litt   0000-0001-6758-7211 L. Fortun-Lamothe 0000-0002-3300-8178.
Primer design Sequences of ITS and β-tubulin from target and non-target species were retrieved from NCBI (www. ncbi. nlm. nih. gov) and CBS-KNAW databases (http://www. westerdijkinstitute. nl) and combined with the sequences obtained in this study. Alignment and primer design used the Geneious software (Biomatters ApS, Katrinebjerg, Denmark). Primers were placed in the most stable positions with the least and the highest degree of variation with target species and nontarget species, respectively. Various features of primers including delta G and Tm values were evaluated by the online Oligo Analyzer software (eu. idtdna. com). UMelt software (https:// www. dna. utah. edu/umelt/um. php) was used to assess the amplicon melt peaks generated by each primer pair. Primers meeting the following criteria were selected for specificity and sensitivity testing: a) Lack of cross-reaction with other target and non-target species, b) Minimum self and heterodimers, c) Melting temperature (Tm) values of 60-62˚C, d) Generating amplicons with distinctive and single melt peaks in multiplex qPCR reactions, and e) Exhibiting satisfactory efficiency and R 2 values.
Coproscopical Analysis Each sample was mixed well and aliquots of ca. 0. 1-0. 2 g of feces were examined by autofluorescence as described before  (20). Samples were considered positive when at least one oocyst could be detected. Differentiation between Cystoisospora suis and Eimeria spp. was based on morphological features and confirmed by sporulation of positive samples  (7). By repeated sampling we estimated to achieve a sensitivity of 80% and a specificity of 95%. Depending on the density of oocysts in a sample semiquantitative scoring of the positive samples (from 1 = low grade to 3 = high grade) was included. In the laboratory of the Institute of Parasitology, fecal consistency was scored from 1 to 4 as described  (21)  and fecal scores 3 and 4 were considered as diarrhea. For each farm a questionnaire was provided for information on farm structure, management, and medication of piglets during the suckling period as well as previous records of diarrhea (Data sheet 1).
Statistical Analysis As management parameters were collected per farm, most analyses were performed on farm level. An association between diarrhea and C. suis infection was also analyzed on the level of litters (n = 603). Fecal score and C. suis-oocyst shedding were compared for all samples (n = 1,206). Testing for significance was done on a 95% CI with Pearson's x 2 test (nominal data) and the Mann-Whitney-U test (metric data). All statistical analyses were performed using IBM SPSS Statistics 24 (IBM GmbH, Ehningen, Germany).
Finally, an identity ranging from 97. 46% to 97. 81% was observed within the five other sequences of Physaloptera species isolated from reptiles and mammals (EF180069, MG808040, JF934734, AY702703, and EF180065). In contrast, the BLAST analysis of the partial (759 bp) cox1 nucleotide sequence showed the lowest values of query cover and identity with those of Physaloptera spp. from the GenBank database, having a greater sequence coverage (i. e. , 98 to 100%) with those of Onchocercidae members than that observed for Physaloptera species (i. e. , 83 to 88%). Among Physaloptera, the highest nucleotide identity values observed were 83. 7% (529/632) with Physaloptera sp. (MH752202) isolated from brown anoles (Anolis sagrei) in the USA, 83. 5% (530/635) with both P. turgida (KT894808) and Turgida sp.