dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We congratulate our colleagues in the  CERN accelerator departments  for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS  and  TOTEM institutes  for their contributions to the success of the common CMS-TOTEM effort. In addition, we gratefully acknowledge the computing centers and personnel of the  Worldwide LHC Computing Grid  and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the  LHC , the CMS and TOTEM detectors, and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ; CAS, MoST, and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES and CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT and ERDF (Estonia) ;  Academy of Finland ,  Magnus Ehrnrooth Foundation ,  MEC ,  HIP , and  Waldemar von Frenckell Foundation (Finland) ;  CEA  and  CNRS/IN2P3 (France) ;  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ; the  Circles of Knowledge Club and NKFIH (Hungary) ;  DAE and DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP and NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE and UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand) ;  PAEC (Pakistan) ;  MES and NSC (Poland) ;  FCT (Portugal) ;  MESTD (Serbia) ;  MCIN/AEI and PCTI (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  MHESI and NSTDA (Thailand) ;  TUBITAK and TENMAK (Turkey) ;  NASU (Ukraine) ;  STFC (United Kingdom) ;  DOE  and  NSF (USA).
Conclusions To the best of our knowledge, this is the first comprehensive dataset that includes most of the Merino and Merino-derived sheep breeds raised in different regions of the world. The results provide an in-depth picture of the genetic makeup of the current Merino and Merino-derived breeds, highlighting the possible selection pressures associated with the combined effect of anthropic and environmental factors. The study underlines the importance of Merino genetic types as invaluable resources of possible adaptive diversity in the context of the occurring climate changes.
Dataset construction and sample collection Blood samples were collected from animals that were as unrelated as possible based on farmers' knowledge, and flock-book, when possible. Genomic DNA was extracted using a GenElute Blood Genomic DNA kit (Sigma Aldrich, St. Louis, MO, USA). DNA quality and quantity were determined using a NanoDrop 2000c spectrophotometer (Thermo Scientific, Wilmington, DE, USA). High-quality samples (i. e. , having DNA concentrations of at least 50 ng/µL and A260/280 ratios of ~ 1. 8) were then subjected to SNP array genotyping. In total, 1694 individuals belonging to 42 Merino and Merino-derived sheep breeds were selected. All the samples were genotyped using the OvineSNP50 BeadChip (Illumina, San Diego, CA). Chromosomal coordinates for each SNP were obtained from the OAR v4.
Data quality control The PLINK software v1. 9 was used for data management and quality control  [42]. The newly generated samples were combined with published genotypes (see Additional file 1: Table  S1 ), applying the PLINK --merge command. The datasets were combined using only SNPs with unique ID and chromosomal positions as identified by the SNPchiMp v. 3 software  [40]. The combined dataset was filtered to retain loci or individuals that meet the following criteria: (1) SNPs with a call rate (CR) higher than 0. 90, (2) SNPs with a chromosomal or physical location, (3) animals with a frequency of missing genotypes lower than 0. 05, and/or (4) SNPs with a minor allele frequency (MAF) higher than 0. 01,  (5)  SNPs that were on autosomes, and (6) animals for which the pair-wise identity-by-state (IBS) between genotypes (based on all markers) was less than 0.
Genetic diversity Prior to the analysis of the patterns of genetic variation and population structure, to mitigate the effects of linkage disequilibrium (LD) between loci the dataset was pruned using the PLINK function --indep-pairwise 50 5 0. 5, resulting in 27,552 SNPs. For each breed, observed (H O ) and expected (H E ) heterozygosities were estimated using PLINK, and the historical trends in effective population size (N e ) were estimated using the SNeP software  [43]  and setting default values and a correction to adjust for LD and small sample sizes. The most recent and distant estimates of N e were taken 13 generations back (N e13 ), and 50 generations back (N e50 ), respectively. Prieur et al. [44]  have reported that the 50K SNP Bead-Chip is not suitable for estimating the N e more than 100 generations back.
Genetic relationship and population structure Multi-dimensional scaling (MDS) plots based on pairwise IBS distances were generated using the BITE R package  [45]  at both the single individual and population levels to investigate the relationships within and between breeds. To assess reticulate relationships between populations, the Reynolds' distances were calculated using the Arlequin v. 3. 5. 2. 2 software  [46]  and subsequently visualised via a Neighbor-Net graph with the SplitsTree v. 4. 14. 5 software  [47]. The same software was used for the computation of pairwise F ST values and their respective statistical significances (P < 0. 05) with 10,000 permutations. The output was visualised using an in-house R script. Population structure was assessed by the model-based approach implemented in the Admixture software v1.
3  [48]  and plotted using the membercoef. circos function in the R package BITE  [45]. Analyses were run for K values ranging from 2 to 45 (corresponding to the number of breeds, plus three additional K values to account for possible sub-structuring within breeds). To identify the best fitting number of hypothetical populations, for each K value, both cross-validation error values and the number of iterations needed to reach convergence were considered. Finally, to explore the genetic relationship and migration events among the breeds, a maximum likelihood dendrogram was generated using the TREEMIX software  [49]. For this analysis, the genotyping data of Sardinian White sheep (SAW)  [17]  were used as an outgroup, and migration edges from 1 to 10 were allowed. The most predictive number of migration edges was selected using the optM function in the R package OptM  [50].
Runs of homozygosity Analysis of high-homozygosity regions across the genome, i. e. runs of homozygosity (ROH), was conducted for each animal using PLINK. The dataset, without LD pruning, consisting of 28,662 SNPs, was used to estimate ROH according to the criteria described by Mastrangelo et al. [51] : the minimum length was set to 1 Mb (--homozyg-kb), and one missing SNP and up to one possible heterozygous genotype were allowed in the ROH (--homozyg-window-missing 1 and --homozyg-window-het 1), the minimum number of consecutive SNPs included in a ROH was set to 30 (--homozyg-snp 30), the minimum SNP density per ROH was set to one SNP every 100 kb (--homozyg-density 100), and the maximum gap considered between consecutive homozygous SNPs was 500 kb (--homozyg-gap 500). The inbreeding coefficient based on ROH (F ROH ) for each animal was calculated as follows: where L ROH is the total length of the ROH in the genome of an individual and L AUT is the specified length of the autosomal genome covered by the SNPs on the chip (2452.
Two extended haplotype homozygosity (EHH)derived statistics  [53]  based on LD were used to detect long homozygous stretches of the genome with high frequencies of particular haplotypes generated by selective sweeps between the two Merino groups: (i) the standardized log-ratio of the integrated site-specific EHH between pairs of populations test (Rsb)  [54] ; and (ii) the cross-population EHH test (XP-EHH)  [53]. The analyses were computed using the rehh package in R  [55]. Haplotypes were reconstructed from the genotyped SNPs using fastPHASE  [56]. Since fastPHASE is based on haplotype clusters, with a size that should be set a priori, the toolkit implemented in the imputeqc R package  [57]  was used to estimate the optimal number of haplotype clusters (K) needed for haplotype phasing. The Imputeqc package is designed to assess the imputation quality and/or to choose the model parameters for imputation.
To do this, the number of times each SNP occurred in a ROH was considered and normalised by dividing it by the number of animals included in the analysis. The top 0. 999 SNPs of the percentile distribution of the locus homozygosity range within each group were considered as potential ROH islands, as suggested in previous studies  [58, 59]. Genomic regions identified by the three approaches for detecting signatures of selection were interrogated for genes annotated to the OAR v4. 0 genome assembly using the Genome Data Viewer provided by NCBI  [60]. To investigate the biological function and the phenotypes that are known to be affected by each annotated gene, a comprehensive search in the available literature and public databases, including information from other species, was carried out. Furthermore, a gene network analysis was performed by adopting GeneMANIA  [61] , using the Homo sapiens datasets.
Dataset During quality control of the initial raw dataset, 44 individuals with low-quality genotypes and six duplicated individuals were excluded. Thus, the working version of the dataset included 1644 animals and 42 populations with an average population size of 40. 26 and a size ranging from 10 (Macarthur Merino-MCM, South Africa Mutton Merino-SMM) to 105 (Corriedale Uruguay-COU). All the animals included in the analysis had highquality genotyping. Additional file 1: Table  S1  provides a summary of the pre-and post-quality control attributes of the working dataset.
Dimension 1 vs 2 (a) and dimension 1 vs 3 (b). The different colours of the squares reflect the three clusters that include breeds with similar genetic background. For a full definition of breeds see Table  1. breeds influenced by Australian Merino strains and (ii) breeds with a typical meat or double purpose, which were found scattered in the MDS plot. The RAM, APA and MCM breeds were distinct and had long branch lengths. To determine the proportion of shared ancestry genomic components, Admixture analysis on the whole dataset was done to separate breeds according to their genetic background and/or geographic origin (Fig. 3 ). The cross-validation error registered the lowest value at K = 43, suggesting that this value is the most probable number of clusters explaining the variation in this dataset (see Additional file 8: Fig.
S4 ). However, admixture patterns, obtained at K ranging from 2 to 5 were first analysed, since they contributed to a better understanding of the ancestry of the investigated breeds. The graph presented in Fig. 3a  shows the early separation of Fig. 2  Neighbor-Net graph based on between-breed Reynolds' distances for the whole dataset. For a full definition of breeds see Table  1  Fig. 3  Admixture software results in a circular fashion for K (number of clusters) = 2 to 5 (a), and geographic distribution of the genomic components corresponding to the K = 5 resolution (b). For a full definition of breeds see Table  1  RAM (in blue, at K = 2). The two Corriedale (COA and COU) are separated at K = 3, and their cluster (in red) is mainly shared with BDC and IDF, while it is poorly shared with the three Australian breeds, the Argentinean, the Uruguayan and the South African Merino and Merino-derived breeds.
Acknowledgements The project was developed within the framework of the  Ad hoc action "Merino and Merino derived breed in Europe: state of the art-in situ valorization" of the European Regional Focal Point for Animal Genetic Resources  (ERFP). We would like to thank  Andrea Rosati  ( European Federation of Animal Science)  for his input towards the design of this study. The authors wish to thank the two anonymous referees fort their valuable comments to the manuscript and their constructive suggestions.
DEM and forest cover Topographic information used in the proposed PRA detection method is classically derived from a DEM. We primarily used the reference 25 m resolution DEM from the French National Institute of Geographic and Forest Information (IGN) as a good compromise to (i) detect the right number and areas of PRAs (even if the detection is then certainly less precise in terms of PRA boundaries than with finer resolutions, e. g. Bühler et al. , 2013) , and (ii) be ultimately applicable over very large areas at reasonable computational costs. Section 4. 2. 3 investigates how using DEMs of finer resolutions (also provided by IGN) affects the results. In addition to the DEM, our method considers forest cover input data. For the French Alps, three nationwide forest cover databases can potentially be used: -The DB forest (database forest) is provided by IGN (French National Institute of Geographic and Forest Information) and obtained from the interpretation of aerial infrared photographs.
A total of 32 classes of vegetation are available (notably, as a function of tree species; ADEME and IGN, 2019). These data were created in 2013. -CORINE Land Cover is a database of land cover based on satellite imagery, which considers five classes of vegetation (artificial surfaces, agricultural areas, forests and semi-natural areas, wetlands, and waterbodies;  Caetano et al. , 2009). It is updated every 6 years, and the 2012 version was considered for the sake of comparison with the IGN data. (Baghdadi et al. , 2021). The 2016 version was selected as the closest to 2013. Our PRA detection method uses forest presence/absence only. We thus derived this information for the three databases in our study areas. None of them is free of errors. Specifi- cally, visual comparison with aerial photographs shows that, logically, the main issues occur when the forest density is low, which makes the limit between forests and non-forest areas difficult to set (Fig.
S1  in the Supplement). The target audience mostly includes mountain professionals  (Ancey, 1996). CLPA now covers most of the French Alps, but some areas within the 23 massifs are still completely uncovered (e. g. less than 40 % of the Mont Blanc massif but nearly 95 % of the Chartreuse massif, Table  1 ). All the information is freely available at http://www. avalanches. fr (last access: 20 February 2023;  Bonnefoy et al. , 2010). Due to its long history, its regular update by devoted technicians, the continuous financial support of the French ministry in charge of the environment and the consideration in the determination of avalanche terrain of a large number of different data sources, CLPA is very reliable, meaning that an avalanche extent which is within CLPA is almost surely a true avalanche extent.
positive rate is in numbers for the massif of Mont Blanc (80 %). This can probably be explained by the fact that avalanche activity is, on average, more exhaustively documented close to release areas within the extents of CLPA records in the massifs of Chartreuse and Maurienne, making the validation sample more comprehensive in these mas-sifs. The reason is that, in the Mont Blanc massif, many avalanche release areas included within the area covered by CLPA (south-east of Fig. 7 ) are located at high elevations and in remote zones and very far from any forest stand. Hence, related avalanche activity was missed during the establishment of CLPA records, as no insights from past snow. This overall result supports in a pragmatic way the usefulness of the different steps of the method and the choices made, even if the effect of one specific filter or data set choice can be relatively minor (e.
g. true positive rates are often quite similar with/without certain filters or with two competing data sources). In more detail, regarding the four different topographic filters (minimal elevation, slope range, minimal area size and maximal distance to ridge) in all three massifs, the minimal area size has a strong impact on true positive rates, notably in terms of PRA numbers. Its influence on PRA areas is much lower because this filter removes many small PRAs, modifying the overall PRA extent only slightly. Also, for the three massifs, the minimal-elevation threshold has a limited impact, on both PRA numbers and PRA areas, with decreases in true positive rates never exceeding 5. 4 %. Yet, without it, a few PRAs are detected at low elevations, notably in rather unrealistic locations (if avalanches were actually released at these locations, testimonies would definitely have been available and included in CLPA records).
Finally, we compared to the default results the results obtained (i) with the two other nationwide forest databases introduced in Sect. 2. 2 or (ii) without any forest cover filter. When forested areas are not removed at all, a much higher number of PRAs covering large areas are detected. This leads to decreases in true positive rates exceeding 20 % in the Mont Blanc and Maurienne massifs. This decrease in the true positive rate reaches 33 % in numbers in the Chartreuse massif, which is logical, as it is the massif where the forest is the most widespread among the massifs we study. This result shows the value of the forest filter to exclude correctly many areas where avalanche release is virtually impossible. Comparing the different forest databases to each other shows that the best true positive rates are obtained with the DB forest from IGN but with drops in true positive rates not exceeding 20 % in both numbers and areas when either of the two other candidates is used.
A detailed analysis in the study area of Chamonix (Fig. 9 ) confirms that (i) for some low-elevation areas, CORINE Land Cover and Theia both indicate dense forests that actually do not exist according to the aerial photograph and (ii) forested areas provided by Theia are, at some Table  5. Marginal effect on true positive rates of the different factors and data sources, Mont Blanc massif. Total area of detected PRAs and total number of detected PRAs are those of the part of the massif covered by CLPA (Table  1 ). For the PRA detection, in each column, only the considered factor is removed/varies according to the column label. All other factors and the DEM resolution are set to their default values (Fig. 3 ). For the determination of the validation sample, all factors and DEM resolution are set to their default values (Fig.
Our parametric study showed higher true positive rates in both numbers and areas when forested terrain was excluded compared to when this filter was not considered. As forest cover is known to be effective at preventing avalanche release, this result is logical. We tested the three main forest data sources available at the scale of the entire French Alps. None of them is perfect, and information that is more accurate may be available in other areas/countries. However, visual analyses in different areas showed that at least the DB forest we retained is acceptable (Fig. 2 ). In addition, the systematic analysis performed over the three entire massifs considered showed that it leads to the highest true positive rates in both numbers and areas with regard to the other available forest data sources.
Evaluation of detected PRAs based on the CLPA/avalanche cadastres In order to evaluate the proposed method, we compared detected PRAs to CLPA after isolating individual release areas within CLPA extents. However, evaluating a PRA detection method and even determining a suitable validation sample are by essence difficult, which contributes to explaining why (i) such validation data sets are rare and generally small and (ii) even proper evaluation exercises are seldom in the literature (e. g. Bühler et al. , 2018). What follows discusses the pros and cons of the approach we chose and provides insights from the work done that may help in designing more efficient evaluation techniques for PRA detection methods. Despite drawbacks inherent to any avalanche cadastre (uncertainty regarding some extents or even missing avalanche extents), CLPA is a remarkable source of information regarding past avalanches  (Bourova et al.
Lastly, CLPA does not distinguish release areas from flow paths and runout zones, which implies that pre-processing is required to isolate individual release areas within CLPA extents that can be compared with our detected PRAs. To this aim, we used the same filters as for the PRA detection. This means that the identified PRAs and our validation data are no longer independent. The way we define our validation sample may therefore be criticized. However, obtaining a fully independent exhaustive sample of ground truth PRA may simply be impossible. A first reason is that even "live", what can be observed is the full extent of an avalanche, and separating its release area from the flow path may be very difficult, so the delimitation of any release area will always involve some uncertainty. Second, assuming that one is able to precisely map a release area, there is little chance that it corresponds to the entire PRA for the considered path.
). This may help in assessing even more precisely the strengths and weaknesses of our (or another) PRA detection method. In addition, we performed the evaluation of our detection method over unusually large areas covering significant proportions of three entire massifs with diverse characteristics. A similar approach could be further used for comparing several PRA detection methods (Sect. 6) and/or in other mountain environments with validation data having strengths and weaknesses different from those of CLPA. This would help in understanding to what extent (i) the high scores we obtain are strongly influenced by our evaluation framework, (ii) these scores can be reproduced in other contexts and (iii) the most critical parameter needs to be changed to fit various conditions (e. g. lower minimal elevation in colder climate).
Conclusion and outlooks To map and mitigate avalanche risk at large scales, the automated detection of PRAs is a powerful solution, notably to provide inputs for avalanche simulation software (e. g. Gruber and  Bartelt, 2007). In this study, a PRA detection method adapted to the characteristics of the French Alps was developed and tuned on the basis of CLPA, a valuable largescale data source regarding past avalanches. In addition to the potential benefit for better understanding and assessing of avalanche hazard in the French Alps and in mountain environments with similar characteristics (see below), outcomes of the work include (i) the determination of individual PRAs using a watershed delineation algorithm; (ii) an approach to define a validation sample from a cadastre of avalanche extents; (iii) an evaluation procedure based on two metrics, PRA numbers and area; and (iv) a better definition of evaluation scores that should be interpreted in the context of PRA identification.
More ambitious outlooks relate to improvements of the PRA detection method: (i) make it dynamic to assess PRAs conditional to snow and weather conditions (e. g. Chueca Cía et al. , 2014); (ii) switch from deterministic to probabilistic detection rules (e. g. Kumar et al. , 2019)  to, for example, include in it the uncertainty about the best parameter values to be used; and/or (iii) use CLPA extents not only as an evaluation support but also as a training sample. Finally, a comparison of PRA detection methods would certainly be beneficial for the community and should be envisioned in the future. To this aim, we hope that our data, now freely available ("Data availability" section), will foster further benchmarks between alreadyexisting or new PRA detection algorithms in order to draw firmer conclusions regarding the respective efficiency of the different proposals in different contexts.