dataesr/scientific-paragraphs-categorization huggingface dataset:
Cluster analysis The optimal cluster number was determined using "NbClust" package  (Charrad et al. 2014). By computing 30 different indexes, optimal number of clusters in a dataset is determined. The function was adjusted for the k-means clustering method, setting the minimum cluster number to 2 and the maximum number to 10. The retained principal components were used as input in the clustering procedure. Once the optimal cluster number was identified, the "kmeans" function was used to allocate the different NUTS2 regions into the previously identified clusters.
DPS typologies High positive correlation was found between the indicators "Average animal number per farm" and "Average farm size by total UAA," and between "Average emission intensity of total GHG" and "Average emission intensity of NH 3 from manure management. " In addition, high negative correlation was found between "Average share of arable land over the total UAA per farm" and "Average share of permanent grasslands over the total UAA per farm. " In all cases, the latter indicator was retained. The results for both KMO and Barlett's sphericity tests show that the database is appropriate for the following statistical analysis. The PCA found that the first four PC cumulate 78. 7% of the variance. More precisely, PC1 accounts for 35. 7% of the variance, while PC2, PC3, and PC4 described 18.
By calculating specific indicators related to the degree of dairy specialization of the regions analyzed and estimating the intensity of regional dairy direct GHG and NH 3 emissions, we contribute to a better understanding of the sector in aspects not contemplated so far due to the lack of specific quantitative indicators. In addition, the joint assessment of representative typologies for dairy and fodder production cooperates in the design and application of adapted policies by considering the diversity of these production systems at the regional scale in Europe. However, further research is needed to integrate into the analysis farmlevel data on diets, crop allocation, and circularity in the context of dairy cattle-fodder production systems. Future database improvements should reflect more specific indicators, and cooperate in the development and implementation of the integrated dairy-crop production systems.
Notably, accounting for intra-national specificities such as feeding regimes and management in GHG and air pollutant inventories, will allow for a better analysis of DPS environmental impacts. In this context, future studies should focus on addressing these interactions at a lower regional breakdown scale (NUTS3), facilitating even more adapted measures. and Food (BLE) under grant number 2819ERA08A ("MilKey" project, funded under the Joint Call 2018 ERA-GAS, SusAn and ICT-AGRI 2 on "Novel technologies, solutions and systems to reduce the greenhouse gas emissions in animal production systems"). BC3-Research is supported by the Spanish Government through María de Maeztu excellence accreditation  2018 -2022 (Ref. MDM-2017-0714)  and by the Basque Government through the BERC 2018-2021 program.
We report here on a multicentre study where we recorded and analysed cumulative records of spontaneous in-cage activity and NH 3 ppm across the cage floor during five consecutive biweekly cage-change cycles. The four participating centres used identical husbandry protocols, and both male and female C57BL/6J mice were kept in digital ventilated cages, i. e. IVCs complemented with an external sensor-board enabling collection of in-cage acitivity data 24/7 (DVC 1 ; Tecniplast S. p. A. )  [2, 52]. Three times a week, ammonia concentrations were measured under flow conditions across the front, middle, and rear of each cage. At one centre, additional experiments were performed assessing in-cage bacterial load, varying cage-change intervals, bedding materials, and numbers of animals per cage. At the end of the experiment, tissues of the upper airways from one randomly chosen animal from each cage was analysed for histopathological changes (n = 20).
International centres This study was conducted at four European centres during summer-early autumn 2018: Karolinska Institute, Sweden (KI site), Leiden University Medical Center, The Netherlands (LUMC site), Institute of Molecular Genetics of the Czech Academy of Sciences, Czech Republic (IMG site), Universitede Grenoble Alpes, France (UGA site). Three centres (KI site, LUMC site and UGA site) recorded animal locomotion data and performed ammonia measurements while IMG site recorded in-cage activity only.
Another two complete cycles were performed to collect animal activity and NH 3 measurements during the bi-weekly regime. DVC 1 data processing, data computations, and statistical analyses DVC 1 metrics. We used activation density metric to measure animal activity in the home cage  [52]. Data (raw data resolution is one sample per 250 msec) were aggregated in bins of one minute. We considered day time activity (average of all one minute bins within the lightson period of each day), night time activity (average within the lights-off period) and, as previously reported  [2] , diurnality, which is the percentage of daily activity performed during lights-on over the total activity during the 24 hours day (considered as the sum of lights on and lights off activity). Nocturnal species like Mus musculus typically display a reversed circadian rhythm  [2].
We determined the latrine position as the area with the highest difference between the average of capacitance readings of the last night of the cycle and the average over the first night of the cycle (for further information see  Fig 5 in S1 File). This procedure was validated towards measured ppm NH 3 and the standard routine where latrine position is determined by visual inspection at cage-change. It should be noted that since the animals were group housed, all activity data presented in this study are the collective floor activity of all mice in the cage.
Statistical analysis To test differences across sites or sexes, cage-change cycles, and days, we performed repeated measures analysis, using the rank-based analysis of variance-type statistic (ATS), as implemented in the nparLD R Software package  [61, 62]. We chose a non-parametric test instead of Repeated Measures ANOVA because normality assumptions were violated in some cases. We considered cages as subjects; time, event, and observation-order as within-subject factors ("sub-plot" repeated factors), and sex and site as between-subject factors ("whole-plot" factor). According to authors' terminology  [55] , we used either F2-LD-F1 (sex and site as whole plot factors and observation order as the repeated factor) or F1-LD-F2 (sex as the whole-plot factor and event and observation order as the repeated ones), and F1-LD-F1 or LD-F1 when only observation order was used as subplot factor.
The statistical analysis of time-series with nparLD is based on rank-order of the observed data, with the relative effect size (p s ) as effect size measure  [62]. The difference towards parametric tests being that instead of the mean difference between observations, the rank-order is used to assess the probability that two sets of observations differ (p s = 0. 5 means that there is no difference in rank-order), and with longitudinal data if the relative effect size varies across the time-series within the sets of observations. Comparison of two independent samples, and paired samples, were conducted using nonparametric Mann-Withney U statistics (U-test and Wilcoxon's test for matched pairs). In these instances we used the common language (CL) effect size statistics  [63] [64] [65] [66]. The CL effect size is based on the rank-order (rank sum) of the observed values, and indicates the relative frequency with which the rank sum from one set of observations will be larger than the rank sum of a second set of observations.
Correlation was conducted with the nonparametric Spearman rank correlation. The Spearman correlation coefficient (r s ) indicates the effect size with a range from a perfect inverse covariation (r s = -1), through no covariation (r s = 0) to a perfect positive covariation (r s = 1) of the ranks for two parameters. We used Python to process and visualize data and R to run the statistical tests. Histopathological analysis by two independent specialists of coded slides from 20 animals (10 of each sex) and 20 germfree animals (same age, both sexes) of the nostril region, middle and rear part of the upper airways included ranking the prevalence (from 0 = negative to 5 = prevailing) of a range of parameters in each of the three regions (see Table  2 ). This set of ordinal data from each of the three regions was first analysed using multiple covariance analysis (MCA)  [67]  controlling for sex and axenic status, and subsequently, run against the in-cage NH 3 ppm and bacterial load records (continuous data) using a mixed model principle component analysis (mixPCA)  [68, 69].
Discussion Here we report the results of a multicentre study at four research institutes in Europe conducted in 2018-2020. The rationale was to shed light on the in-cage activity of a commonly used laboratory mouse strain across a cage change cycle of two weeks and to what extent incage levels of NH 3 impacted animal activity, dynamics of the use of cage floor space relative to the location of the latrine, and health of the mice. Another objective was to analyse to what extent the data reproduced across facility sites. This is the first report of a study involving the measurement of in cage NH 3 concentrations, where in each cage measurements were taken at three positions across the cage floor covering the entire width of the cage at all three positions under IVC flow conditions. In-cage ammonia concentrations were measured at different time points between cage changes using a commercially available portable electrochemical sensor device  [41].
In all cases, the latrine position was determined by visual inspection as well. Our data show that even though food and water were presented more towards the rear of the DVC 1 GM500 cage type used and the rear of the cage was the more shaded area of the cage, male mice showed no clear preference for creating the latrine in the front or rear of the cage while female mice showed a preference for the front of the cage. Only at IMG, both male and female mice had a preference for the front of the cage inspite of the higher light intensity measured across the front of the rack compared to the other centres. This finding could not be linked with a causing factor except that that rack was placed against a wall bordering a corridor albeit with low traffic intensity and no detectable transmission of sound in the audible range for humans.
Acknowledgments We thank  Dr. Inken Beck  and the  Animal Research Center, Ulm University, Oberberghof, Ulm, Germany , for providing the germfree mice. Gianpaolo Milite  (Scientific Consultant,  Udine, Italy)  for organising the germfree study and critically reviewing the manuscript,  Martijn Bolderheij  who performed the experiments at the  LUMC , the animal facility staff and animal technicians at the four sites involved in the studies, and  Tapvei  for providing the bedding at all four sites.
3. Treatment BUBBLE + TIME: both bubbles and time were predictive of feed delivery. Fish received daily feedings at the same time schedules as treatments TIME. Each feeding was also systematically preceded by 15 s of bubbles (as treatment BUBBLE). 4. Treatment RANDOM: neither bubbles nor time were predictive of feed delivery. Daily feedings were delivered according to the same randomized schedule as the one used for treatment BUBBLE. The bubbles were systematically delivered separated from at least 15 min from feedings. Group behaviours during the 12 days of conditioning. Previous studies showed that fish activity was a relevant behaviour as an indicator of anticipation of feed delivery  50. Thus, group activity in the rearing tank was automatically analysed by EthovisionXT® software (v. 15.
0. 1418) (Noldus, Wageningen, The Netherlands) (Table  1 ). Activity was given as a proportion of pixels that changed intensity between two successive images (25 images per second). Only pixels within the defined area (= 100%) are taken into consideration. As a result, activity data represent global activity of the group of individuals. Data are given as percent of activity (%). Behavioural recordings were conducted at three different feedings or bubble sequences (1st, 3rd, and 5th sequences of the day) on the 1st and the 12th days of the conditioning to examine its progression over the experiment. Depending on the period targeted, analyses were performed into the bubble area or into the whole tank (Fig. 7C ). Fish ability to use bubble diffusion as a predictor of feed delivery was assessed by analysing their activity during the 15-s sequence of bubble diffusion added to the 5 s of time gap that followed.
We analysed the first 20 min, by 5 min time-bins with the Ethovision ® XT software. At 23 min, individuals automatically received 80 feed pellets (approximately 100 mg) and the remaining pellets were counted after 5 min to measure a possible inhibition of feed intake (anorexia) following a stressful situation (novel tank exposure). Indeed, feed consumption recovery after an acute stressor is a classical stress indicator in fish  37. The following behavioural parameters resulting from the automatic tracking of each individual were scored: total distance moved (in cm), maximum swimming velocity (in cm/s), angular velocity (in °/s) (i. e. , erratic swim), and time spent (in %) in the border zone (i. e. , thigmotaxis). Weight and size of fish. Fish body weight (W) and length (L) were measured after the novel-tank test under anaesthesia (50 mg/L tricaine and 50 mg/L sodium bicarbonate).
number + (1|Rearing tank). During feed omissions tests, group activity was analysed with treatment and omission number (1, 2 or 3), and their interaction, as fixed factors. The following LMER was used: lmer(Mean. activity ~ Treatment*Omission. number + (1|Rearing tank). Similar to the previous model, rearing tanks were added as random factors. The day (day 1 or day 12) was not included as a fixed factor into the models since data given by Ethovision did not account for fish growth between days 1 and 12 of the experiment, resulting in more pixel changes on day 12 compared to day 1. All data were checked for normality before running a model and the dispersion of residuals was subsequently checked. If data did not meet the assumptions for parametric statistics, data transformation were applied else by inverse, log-or log10-transformation.
Acknowledgements This work was financially supported by the  French funding account CASDAR (Compte d' Affectation Spécial "Développement Agricole et Rural")  under grant agreement n°  19AIP5919  and JUNIA. We also thank  Dr. Guus Bongers  from  Noldus , which holds the licence for the EthovisionXT® software, for his help on how to use the activity analysis on the software.
With respect to the trend in the number of chamois publications in proportion to those on Caprinae, publications increased more rapidly for chamois than for mountain ungulates as a whole, and chamois publications accounted for 16% of all Caprinae publications in the study period (Fig. 2 ). Major biases emerged in the number of publications when species and subspecies were taken into account, although these broadly reflect the relative abundance of each species/ subspecies (cf. abundance data in  Corlatti et al. 2022a). About 80% of all chamois publications between 1980 and 2020 mentioned the northern chamois R. rupicapra (n = 608), while the southern chamois R. pyrenaica was mentioned in only 28% (n = 208). Differences were even more evident when considering subspecies. For the same time frame, only 8.
Conclusions The Caprinae subfamily consists of species with a great diversity of forms and behaviors, the result of complex eco-evolutionary scenarios favored by radiation towards very different ecological conditions (cf. Schaller 1977). In many respects, the chamois occupies an intermediate position in the evolutionary pathways between forest-dweller, monogamous and monomorphic goat antelopes such as the serow Capricornis spp. and highly dimorphic, highly polygynous wild sheep Ovis spp. inhabiting open habitats  (Geist 1987). Accordingly, the chamois is a very interesting target for ecological investigations, but at the same time a great effort is needed to collect data over large geographical areas to enable the extrapolation of results to other populations. Research on the genus Rupicapra has markedly increased over the last decades, contributing substantially to our knowledge of the biology and ecology of mountain ungulates.
2: increasing data collection and analysis capabilities The use of precision measurement tools to collect data is the second step in the precision implementation process (Figure  2 ). Data collection implies that the appropriate technology has been identified to measure a specific metric at a given interval. Successful data collection requires a consistent timestep (e. g. , daily) to ensure data quality. Humans, sensors, or robots may collect data. Each provides information about the status of a biological trait (e. g. , body weight measured on a scale) or of its proxy (e. g. , a surface measurement from an image as a proxy for body weight). Often measurements or proxies are combined (using modeling) to determine a metric that is not directly measurable by humans or precision devices  (Jacobs et al.
, 2022, companion paper). In considering precision measurement devices, it is important to consider the application and need for data storage and integration, data-processing algorithms, and potential sources of bias in data collection and algorithm development. Automated data collection provides a large volume of data. These data must be processed before being used and/or stored in a database in most cases. This includes cleaning, validating, and reconciling missing and abnormal values. If multiple data are being integrated simultaneously, which is often the case, the data must be further parsed and organized into a consistent format. This requires adequate governance, security, and meta-data, making data management one of the largest and most difficult challenges for precision data collection.
, 2019 (Ellis et al. , , 2020)). Traditionally, MMs have been used to understand, illustrate, and support animal production  (Ellis et al. , 2020). However, their limitations have become more apparent over time as the velocity and volume of data have increased and the available data expand from numerical values alone to diverse sources such as image and audio files. As the complexity represented in MM increases, so does their reliance on large databases (e. g. , the National Academies of Science and Engineering and Medicine nutrient requirements and related nutrition equations; NASEM, 2016). Many national databases have application programming interfaces that enable them to be linked with MM, allowing for more rapid updates as the current body of animal nutrition knowledge advances  (NASA, 2021; NRCS, 2021).
Unfortunately, many published MM equations and parameters are difficult to reproduce  (Jacobs et al. , 2022 companion paper) , although they have laid the foundation of animal science modeling. This problem is exacerbated by older programming languages (e. g. , Fortran) that are not intuitive and difficult to translate into updated models. The potential dependence of databases should be considered when developing MM for precision livestock systems; in essence, it is a balance between data-hungry models and modeling capabilities. The challenge of automatically updating key input parameters highlights the growing role of real-time data collection (step 2). Applicability of MM is also influenced by the degree to which unmeasurable, yet significant parameters are reasonably accounted for, parameter variations in testing either meet or exceed observable ranges, and failure to consider non-measured parameters associated with realtime measures.
Nevertheless, model choices about the type (mechanistic vs. empirical) and data resolution (historic vs. real-time) involve trade-offs between fidelity and efficiency. It is important that the appropriate model or combination of models for PSM be used to minimize performance gaps (step 1). This requires that PSMs quickly obtain and integrate the various types of data from precision measurement devices and search for an optimal solution. This is one reason that most real-time models have a data-driven component (Table  1 ). It is also critical to understand why some livestock models can stay current and others become obsolete, such as over-reliance on large databases, so that integration with production systems does not overburden management with superfluous tasks outside normal model use and maintenance.
Backfat thickness, which is used in combination with body weight to determine the status of body reserves, can be measured simultaneously. The use of automatic weighing scales in the feeding stall allows much more frequent data to be obtained, and accelerometers can also be used to evaluate the physical activity of sows  (Ringgenberg et al. , 2010). However, this type of "mechanical" equipment may be challenging to maintain long-term, and video and image analyses may be a more promising, robust alternative for real-time evaluation of body weight  (Cang et al. , 2019) , activity  (Ahrendt et al. , 2011; Labrecque et al. , 2020) , and perhaps body condition. The interest in precision feeding strategies for gestating sows was evaluated through simulations by  Gaillard et al. (2020b). In that study, a conventional 1-phase feeding strategy was compared with a precision feeding strategy, which consisted of mixing two diets with low or high nutrient content.
, 2021). Such tools generate large datasets that need to be analyzed and interpreted to create benchmarks for phenotypic traits  (Puillet and Martin, 2017). For instance,  Abdelkrim et al. (2021)  developed a perturbed lactation model in dairy goats incorporating representation of perturbations. This model is a valuable decision support tool as it allows the characterization of the potential milk production of a dairy goat (i. e. , Saanen or Alpine) throughout lactation in a non-limiting environment as well as the depiction of the deviations induced by the on-farm conditions. The deviations represent the ability of the lactating goat to cope with environmental challenges. However, the main limitation of this model is the dependency on the data quality to avoid confusion between the deviation related to the environment and the low accuracy of the data recorded  (Abdelkrim et al.
, 2021). Other precision technologies like real-time weighing may be used synergistically with remotely sensed data to more precisely adjust stocking rates relative to forage resources and animal requirements. Real-time weighing is possible using precision walk-over scales located within pastures that measure daily individual weight. These weight data have the potential to improve model inputs when using equations like net energy for gain (NASEM, 2016; Figure  3 ) instead of an estimated body weight. Thus, incorporating virtual fencing technology into precision pasture management allows land managers the ability to draw fence boundaries based on environmental factors that are derived from other precision measurement technologies (e. g. , remote sensing) such as elevation, soil type, plant communities, animal weight, and forage quality, giving greater control over animal movement on the landscape and potentially allowing animals to capture the highest quality forage (Figure  3 ).