dataesr/scientific-paragraphs-categorization huggingface dataset:
e. it does not cover many extra values in the interval, which is due to properties of the normal distribution and due to ignoring between-assay variability. Empirical percentiles cover a defined proportion of available data. They are relatively easy to calculate and communicate. They are related to the range, which covers the full distribution. Empirical percentiles do not incorporate distributional assumptions for the underlying dataset and, notably, do not take into account variation to estimate future values. They look backward and are robust in the regard that they are also not biased by incorrect distributional assumptions, such as parametric estimates. Hence, empirical percentiles describe datasets of unclear distributions robustly and are resilient against potential outliers within the dataset, which affect variance estimates of the parametric intervals.
A confidence interval contains the population mean of a concurrent population with a pre-specified confidence probability. It is a measure of precision for the estimated population mean; hence, additional concurrent data increase the precision of the estimate, i. e. it shrinks notably towards the mean with increasing n. This is why a confidence interval is not a good interval for HCD, as it does not describe the range of probable values. A tolerance interval contains a specified proportion of future samples with a pre-specified confidence probability, where the number of future samples is not specified. Such an interval is very helpful, for example in production processes or quality assurance to cover, for example, 99% of future values with 95% confidence. A prediction interval contains a single future observation within n future samples, with a pre-specified confidence probability.
The use of prediction intervals considering between-assay variability was previously proposed  (Hothorn, 2016; Hothorn and Kluxen, 2020a). Such methods relate to the derivation of "cut points", to identify anti-drug antibody positive specimens, for which prediction intervals were also suggested  (Hoffman and Berger, 2011)  and statistical methods and software solutions (R package mixADA) also exist for complex experimental designs  (Schaarschmidt et al. , 2015). One issue in toxicology, as highlighted above, is that many parametric statistical approaches, including prediction intervals, are unstable for low observation numbers, such as n < 5  (Hothorn and Kluxen, 2019; Hothorn and Kluxen, 2020b). The differences of various interval approaches are reviewed in detail elsewhere  (Hahn and Meeker, 1991; Schaarschmidt et al.
05  (Kennedy-Shaffer, 2019; Salsburg, 2002)  and is similarly not data-driven. Relevance may be considered by applying different BMRs for different endpoints, which may be challenging in practice  (Dekkers et al. , 2001). However, the use of HCD to set appropriate BMR values would seem like a suitable method and should be discussed within the regulatory community. There are, at least, three other ways to incorporate relevance thresholds when using BMDs: 1) potency classes can be defined based on control substances or more pragmatic approaches, e. g. dose-ranges as for the LLNA; 2) historical control data can be incorporated into the BMD estimation procedure  (Jensen et al. , 2019) ; or 3) BMD estimation can be combined with relevance/efficacy thresholds.
Discussion HCD supplement toxicological assessments and, when used in a weight-of-evidence approach, serve to maximize the information that can be extracted from a given body of data available for hazard assessment. As such, they may be considered to contribute to the 3Rs principles in the case of vertebrate studies. Kramer and Font (2017)  previously reviewed and explored the possibility to use HCD to reduce animal numbers in the context of 3Rs. HCD are also proposed to be used in clinical trials  (Ghadessi et al. , 2020; Viele et al. , 2014). Best practices on using HCD have previously been described in general and with respect to genotoxicity assays  (Hayashi et al. , 2011)  and proliferative lesions from chronic rodent bioassays  (Keenan et al. , 2009). HCD are used to address different scientific and regulatory concerns.
It should, however, be discussed whether this benefit outweighs the simple application and communication of ranges in practice, considering that there are also difficulties associated with parametric approaches. Most notably, prediction methods are statistically more complex and may be more difficult to communicate and peer review; the methods may also not be stable for the typical low observation numbers in toxicology. The assumed distribution in parametric approaches is critical for their performance, which may be problematic in case of limited HCD. Similar issues are associated with selected confidence levels, or the amount estimated to be within the HCD for tolerance intervals. There are currently no data indicating that one method is better than the other, considering that toxicological procedures use multiple lines of evidence for assessments.
While it is obvious that between-assay variability should be considered when setting HCD limits based on parametric approaches, there may be practical limitations with respect to harmonised databases. Many laboratories collect HCD differently based on the assay, sometimes only as summary statistics without directly accessible individual values. While simulation approaches  (Hothorn et al. , 2019)  allow the generation of pseudo-replicate values, they again critically depend on the assumed distribution. Several points were identified that would benefit from a wider stakeholder discussion (see also Table  3 ). It should be discussed together with research facilities collecting HCD whether a harmonised database format, considering individual responses, would support novel statistical approaches and assessments.
Such databases would be especially helpful to consider between-assay variability, which would be necessary for parametric statistical methods to ensure correct interval sizes. Further, the added benefit of using uncertainty estimates when assessing an effect with respect to HCD or biological relevance limits should be discussed, i. e. whether there is substantiated concern that uncertainty needs to be accounted for to obtain a robust assessment. Such approaches may be especially helpful when discussing fringe effects of results that are used for binary classification  (Kluxen, 2020; Leontaridou et al. , 2019). Non-binary criteria to consider HCD would help the assessment of rare observations or assays that are not commonly conducted, i. e. a continuous measure of HCD appropriateness for the current assay or HCD relevance for an assessment needs to be weighed.
An HCD rejection based on generic criteria may inadvertently reduce the amount of relevant data. A compromise between the extremes of having individual data for all endpoints, which should be preferred by all stakeholders but might not be possible in the short term, as discussed above, and the range as a very simple summary statistic is to describe HCD with multiple point summaries. One approach could involve stating multiple percentiles (e. g. 5th, 25th, 50th, 75th, 95th) and the mean, which indicates the shape of a distribution, without strong parametric assumptions. The alternative is to consider parametric intervals, which can also incorporate betweenassay variability. Graphical approaches that consider the HCD pattern over time could be ideal to assess single endpoints; however, their generic application for in vivo studies considering dozens of endpoints may be impractical.
Better description Ideally, individual HCD are available and can be used for graphical display or to relate concurrent effects to historical variation. This may not always be practical or possible. In those latter cases HCD should be described better than only by ranges. At least a mean value should be supplied or, still better, multiple point estimates or percentiles that describe the HCD distribution in more detail. Creating a harmonised database format A harmonised data format independent of specific assay types would allow a systematic and transparent use of HCD, which could also be updated when new data are available.
Procedure After receiving authorization from the management of a number of nursing homes (in France), people eligible to participate in the study were preselected according to inclusion criteria. Each participant was informed that their participation in the study was voluntary and anonymous. The participants individually signed a written consent to participate and to be enrolled. Data were collected using codes composed of a combination of letters and numbers to protect the participants' anonymity. We used the profession abbreviation followed by a number according to the order of participation in the interviews. The duration of the semistructured interviews was between 30 and 90 minutes.
2. 3. Data analysis As our objective was to analyze the representations of the caregivers on what weighed in favor of or against the implementation of an animal-assisted intervention, and also their experiences, we retained a method of qualitative analysis. Two psychology researchers analyzed the data using qualitative content analysis techniques32,33. The data analysis was carried out in four stages: 1) After transcribing the interviews verbatim (pre-analysis), the researchers first read the interviews in order to understand their apparent message33. 2) The second step was a categorization operation. During the process of content analysis of verbatim explanations, categories (codes) were generated inductively using the interview material. 3) Then, we grouped the codes into potential themes.
Two main themes appeared and were selected to improve understanding and to structure the results: 1. Brakes and 2. Levers. 4) During the fourth phase, the raw data was processed to identify significance and to validate it. The extracted text segments were interpreted individually by each researcher, and then jointly32,33. 5) The emergence and repetition of themes and codes from one source to another gradually made it possible to develop a thematic tree (See: Figure  1 ). This corresponds to the architecture of the themes and codes identified in the scientific and professional literature following the completion of coding operations. The thematic tree includes central themes (theme) and categories (codes), with their respective occurrences.
g. , aggressive honeybee colonies show a high winter survival rate and docile social spiders grow faster than their aggressive conspecifics) (see  Modlmeier et al. , 2014)   [9]. Group behavioural diversity has also been studied in primates. Using all occurrence hunting behaviours,  Samuni et al. (2020)   [10]  documented bonobos' hunting and feeding patterns. Kühl et al. (2019)   [11]  studied 31 specific behaviours in 144 communities of chimpanzees and showed that group behavioural diversity was decreased when wild chimpanzees lived in areas with high human impact. The authors used presence and absence data on those 31 known behaviours from 46 chimpanzee communities and complemented their data set with additional information on another 106 chimpanzee groups using published scientific literature.
Statistical Analysis Statistical analyses were done with the programme R, version 4. 0. 3 [55]. Behavioural diversity is defined here as the number of different behaviours observed in total and for each behavioural category during each observation session. The behavioural diversity in this study was analysed using generalised linear mixed effects models (GLMMs), with the "glmmTMB" function from the "glmmTMB" package  [56]. As behavioural diversity variables were numerical count data, models were fitted for "Poisson" distributed data (log link). For all models, we included the identity of the dolphinaria and the observation day as random factors to account for nested measurements. Predictors included the time of day (i. e. , morning, midday or afternoon), the observer location (i. e. , underwater, above water or both), the size of the group (from 2 to 9), the group sex composition (i.
The number of individuals present allowed for accounting for stochastic size effects. A total of seven models were run with the response variables being (1) the total behavioural diversity, (2) the affiliative behavioural diversity, (3) the agonistic behavioural diversity, (4) the solitary behavioural diversity, (5) the play/exploratory behavioural diversity, (6) the sexual behavioural diversity and (  7 ) the maternal behavioural diversity. Over dispersion and collinearity were checked and revealed no problems, an additional residuals distribution diagnosis was conducted using the "DHARMa" package  [57]  and model selection was achieved using the "MuMIn" Package  [58]. For each response variable, among all tested models, the one with the lowest Akaike information criterion  [59]  was selected.
Acknowledgments: The authors would like to thank the  European Association of Aquatic Mammals  and the  Welfare Committee  for initial discussions and all the participating facilities and their professional care staff. A special and deep thanks from the authors to  Birgitta Mercera , curator of  Parc Asterix dolphinarium (France)  for her constant and enthusiastic support and her professional involvement in dolphin welfare. They also thank  H. G. Rödel  for his help setting up the statistical analyses and  Luisa Kaissner  for her assistance during observations in Malta.
Similarly to previous work, the necessity for an initial adaptation period of the animals as well as for an essential data cleaning procedure of the raw database automatically collected by the WoW, were corroborated. Adaptation of naive ewe lambs enabled the required voluntary passages across the weigh platform and a high volume of individual and daily data after 2-3 weeks. Close monitoring of individual growth was then possible after performing sound data cleanings. A strong concordance of WoW LW data with the gold standard (a standard static scale) LW reference data was demonstrated. At the individual level, even with the lowest number of LW values collected with WoW, it was possible to monitor variations in LW at daily intervals. The establishment of an early warning system to help farmer decision making could therefore be possible.
, 2012; González-García et al. , 2021 , 2018b; Morris et al. , 2012; Polat et al. , 2013). The objective of the current work is to push forward the state of the art on this matter, by testing the use of the WoW for monitoring the progression of individual LW (growth rate) in recently weaned ewe lambs under grazing conditions. We hypothesised the possibility of collecting a large number of longitudinal LW records with the WoW. After an indispensable raw database cleaning process for filtering spurious LW values, a precise and timely LW monitoring would then be possible, either at the flock or individual levels. The last would enable seeing the growth trajectory with greater precision and greater resolution along the time period of interest.
Phase II: Fully automated data collection The second phase lasted 10 weeks (Figure  2 ) and aimed to evaluate i) the possibility of using the WoW system for growing animals from this Mérinos d'Arles breed under grazing condition and ii) the feasibility of automatically measuring their LW with full confidence and precision, and the resultant calculations of individual growth rates (Average Daily Gains -ADG, g/d). The progression in the N°Pass and PlausLW variables were also recorded. The GS measurements continued to be performed every Tuesday at 9:00 am and the data transfer was performed almost every day at the same hour (9:00 am) when no lamb was on the platform. During this second phase, a total of 25172 WoW records were collected.
Database development and outlier detection Daily downloaded raw WoW-records were registered in CSV file format on a laptop (see Table  1  for specifications of the data sets). Data from the GS measurements were available in another CSV file. Using the GS, individual lamb ADG between each two weekly measurements was calculated. Thanks to the calculated ADG, each daily LW was estimated for each ewe lamb. Before further analyses, different data filtering procedures were carried out with the R software (R Core Team, 2021) using the dplyr package to manipulate data. Firstly, records were removed if they did not capture the RFID identity of the individual, or if the registered LW was equal to zero. Then, a threestep data filtering approach was followed to detect and remove lamb misbehaviours and outliers, respectively.
The first filtering step was performed at the group level. Records falling outside the LW range (i. e. , minimum, and maximum) of the group (i. e. , classified as misbehaviours) were removed by detecting extremely low and high values as well as data higher than twice the LW mean of the group (e. g. meaning that more than one lamb was at the same time on the platform). The second filtering step was made at the LW subgroup level. All the data outside the interval [group weekly minimum of LW -2. 5 kg; group weekly maximum of LW +2. 5 kg] aimed to take into account LW fluctuations during the day due to the content in the digestive and urine tracts  (INRA, 1989) , but exclude weight data beyond this range. Finally, a third filtering step was carried out at the individual level. The daily estimated LW of each individual, used as a reference value, was first calculated after calculating ADG thanks to the available weekly LW records obtained with the GS measurements.
Then, all values falling out of the individually accepted range (i. e. , daily estimated LW based on GS ±2×SD) were removed from the database. At the end of the three-step data filtering approach, the result was a cleaned database able to be further processed and interpreted. For each step of the filtering process, the concordance of the records obtained with the WoW and the GS data was evaluated using the concordance methodology proposed by  Bland and Altman (Bland and Altman, 1999). The bias coefficient of concordance evaluates the repeatability of the system. The Lin's concordance correlation coefficient (CCC) was also calculated to evaluate the extent of agreement between the WoW and the GS method  (Lin, 1989). The CCC combines the measurements of accuracy and precision to define how far the WoW-data deviate from perfect concordance (i.
LW data procedure and analysis A complex mixed model was followed based on the interacting effects of the LW measurement system (n= 2; WoW vs. GS), the time (measurement week) and the LW subgroup (Heavy, Average and Light), and based on the nested effect of the individuals in each LW subgroup. The LW monitoring week, the LW subgroup and their multiple interactions were considered as the fixed effects in the model. Interactions between the LW subgroup and the LW measurement system were taken into account to check if any difference exists in the information provided by the system according to each LW subgroup The weekly individual ADG was calculated for each animal using its LW records collected in a time-phase (i. e. , between two weekly GS sessions for the LW reference data, and between the same days for the WoW with the measured or estimated Tuesday data).
Database filtering outputs A summary of the number of records obtained in the WoW database before and after the cleaning procedure is shown in Table  3. From the total of 21 days during the adaptation phase, 6 days were excluded due to battery dysfunction issues. A total of 5411 records were initially downloaded and 1429 (i. e. , 26. 4%) were retained after the removal of LW spurious values (misbehaviours and outliers), which represent an average of 95 records/effective day during the adaptation phase. During the second phase of the experiment (data collection), a total of 25172 records were collected during the 65 effective days (from the total 74 i. e. , 9 days lost due to battery issues). 38. 7 % of those data were retained after the filtering process, representing in average 150 records/effective day -i.
Validation of the WoW system Validation of WoW was made after the adaptation of the animals, on the second phase of the experiment. Table  4  presents the descriptive statistics, the linear regression and CCC reports. The linear regression coefficient of the LW WoW measured values explained by the GS reference estimated values is about 0. 92 with a 1. 181 kg of error (Figure  3 ). The CCC (0. 94) shows moderate agreement between the two LW measurement methods. On the raw database, the Bland and Altman concordance coefficient is -6. 95 ±30. 12 kg, which it is considerably improved after the full cleaning process (i. e. , -0. 83 ±2. 32 kg on the final clean database after the threesteps procedure; Figure  4 ).
Individual level Due to the validation of the WoW-data and the absence of significant differences between WoW and the GS reference scale for the flock and subgroup data, the individual growth monitoring with the WoW is considered as possible. Three extreme ewe lambs were chosen as examples of the individual growth monitoring i. e. , the individuals identified as number 3537 (with the higher number of correct LW recorded), 3601 (with the smaller number of correct LW recorded) and 3954 (ewe lamb dead during the experiment; Figure  7 ). The graphs show the LW monitoring with all data measured. For the lambs 3537 and 3954, there is more LW values obtained with the WoW than with the GS. Their growth rates curves show similar shapes whatever the LW measurement system. The graph for the ewe lamb 3954 shows an abrupt LW decrease since March 19 th using the WoW, whereas this visual information is recorded only after March 25 th with the weekly LW data recorded with the GS scale.
The process used in this study was carried out in three stages, the last stage corresponds to the removal of outliers at the individual scale according to the mean interval ±2×SD. In a previous report by our team  (González-García et al. 2018a)  80% of the raw data are eliminated with this method at the individual scale. In the current study, fewer (61. 3%) of initial raw data were removed. Brown et al. (2012)  reported to retain the harvested WoW records within an interval of 10% of the predetermined LW. Data eliminated in our study correspond to misbehaviour of the lambs on the platform. Despite the "S" structure designed for the animals to pass one by one  (González-García et al. , 2018a) , often two animals were climbing together on the platform. For  Brown et al. (2014a) , these misbehaviours cause low repeatability of the system when they are not removed.
In our study it was then possible to estimate LW of the flock (and according with LW subgroup) by using the WoW. Close individual LW values to those measured with the GS reference static weighing, and without significant difference, were obtained. Using the WoW, it is possible to observe variations over time more precisely due to the quantity of data recorded. At the beginning of phase II, the ewe lambs were in good physical condition i. e. , the LW at 127 ±6 days corresponded to a LW slightly higher than those expected at this age for the Merino d'Arles breed  (Bénévent et al. , 1971). The growth rate of the animals, estimated at 123 g/d and 84 g/d respectively by the static GS weighing scale the WoW, is similar to the ADG reported for Merino d'Arles lambs between 130 and 160 days  (Bénévent et al.