dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulations In  [21] , which is the foundation to this study, we provide the reasons why we used the CupCarbon platform for simulations, described how the simulations were performed using CupCarbon, and also provided results that supported the validity of the two-node approach in demonstrating distributed data fusion in WWPM. We conducted several simulations on the setup depicted in Figure  4  to assess the performance of each of the selected DKF algorithms. Firstly, we ran simulations to evaluate the estimation accuracy of each DKF algorithm by computing the root mean squared error (RMSE). Finally, we then performed simulations to evaluate the leak detection performance of each DKF algorithm by loading the sensor nodes with datasets that contained a known number of leak and noleak events.
Simulations In  [21] , which is the foundation to this study, we provide the reasons why we used the CupCarbon platform for simulations, described how the simulations were performed using CupCarbon, and also provided results that supported the validity of the two-node approach in demonstrating distributed data fusion in WWPM. We conducted several simulations on the setup depicted in Figure  4  to assess the performance of each of the selected DKF algorithms. Firstly, we ran simulations to evaluate the estimation accuracy of each DKF algorithm by computing the root mean squared error (RMSE). Finally, we then performed simulations to evaluate the leak detection performance of each DKF algorithm by loading the sensor nodes with datasets that contained a known number of leak and no-leak events.
Introduction In this section, we study the influence of the distance from the leak position and the size of the leak on the vibration data collected from the pipe surface using the LSM9DS1 accelerometer. The objective is to determine the maximum distance from which the accelerometer sensor should be placed from the leak position on the pipeline to effectively detect the presence or absence of a leak and the size of the leak that can be detected by the accelerometer. To study how the distance from the leak position affects leak detection, we varied the distance of the sensor from the leak position and collected vibration data at distances of 0. 25 m, 0. 5 m, 1 m, and 2 m from the leak position. For each distance from the leak position, we varied the leak size by tuning the size of the leak valve, measuring the flow rate, and then observing how it influenced the leak detection.
Results Figure  7  represents the magnitude of the resultant acceleration of the vibration measured at different leak distances (0. 25 m, 0. 5 m, 1 m, and 2 m) from the leak position and leak sizes (7 L/min, 15 L/min, and 30 L/min). It is difficult to identify the data corresponding to no leak because the amplitude of the vibration is very close to zero. For each leak distance and leak size, 25,000 samples of vibration data were collected for a time duration of 27 s. The results revealed that, as the distance from the leak position increases, there exists no significant difference between the leak data and the no-leak data. As shown in Figure  7 , there was a significant difference between a leak scenario and a no-leak scenario for distances of 0. 25 m, 0. 5 m, and 1 m from the leak position.
most common in WDN, the detection distance will be lower than the 1 m which we obtained in our experiments since we used above-the-ground (surface) pipes. The reason is, there is a reduction in the vibration generated by the leak due to absorption by the surrounding soil  [37]. The detection distance obtained from using surface pipes in this study gives us a first-hand idea of the maximum separation distance which can be expected in the case of buried pipes, which will be used in future experiments, since we will be dealing with a real WDN. To obtain the baseline value, 86,400 samples of acceleration data were collected each day for a period of 3 days and an average was taken to obtain the baseline value when there was no leak in the pipeline. To deal with the noise when computing the baseline value, we applied a Kalman filter on the data before averaging the data collected for the 3 days period.
Data processing and analysis We created a custom reference for cell counting using the 10x Genomics Cell Ranger v. 4. 0. 0. 71 command mkref following default settings and the well documented CellRanger pipeline. For that, we used the publicly available genome. fasta file (v. 1. 4) of the collared flycatcher and the collared flycatcher annotation. gtf file from Ensembl (v. 1. 4). Count matrixes for every gene in each individual cell for the three samples were obtained. Using this output and the. gtf file, a. loom file was generated for each of the three samples using the command run10x from the python package Velocyto v. 0. 17. 17  72. Finally, this file was exported to Seurat v. 3. 2. 0 73 for filtering, normalization and clustering (described below).
Velocity analysis To confirm the identity of the clusters with another analysis and infer the most likely time trajectory, we used the python package scVelo  76  to run velocity analysis, pseudotime analysis and a PAGA (Partition-based graph abstraction) graph  77. With that purpose we exported our Seurat object to. h5dr format compatible with scVelo and followed their welldocumented pipeline. Once all our clusters were assigned to one of the main stages of spermatogenesis, we performed a comparison with mouse and human using one to one orthologs. We downloaded the complete list of markers found by Hermann et al. for mouse and human and we grouped the clusters in the three main stages of spermatogenesis and identified the shared markers among flycatchers, mouse and human. The shared markers were displayed using Venn plots.
In order to visualize the DE genes, volcano plots were computed using the package enhanced volcano. We performed a hypergeometric test on the DE genes of each cluster to test for over-representation of Z-linked genes using the R function phyper. To test whether there was a significant difference of DE genes between the stages, we implemented a generalized linear model with binomial distribution of the response variable. We used the cbind function to consider DE genes or non-DE genes as binary response variable and we used spermatogenesis stage as explanatory variable. Finally, we performed a GO analysis on the DE genes using the same methods described above.
Acknowledgements We are thankful for help during the collection of the data and/or the development of the manuscript to  William Jones ,  Katerina Guschanski ,  Tom van der Valk ,  David Weatcroft ,  Mahvash Jami ,  Javier Florenza García  and all Qvarnström lab field assistants. We also thank the  Microbial Single Cell Genomics Facility at SciLifeLab  for help developing the cell suspension protocol and for letting us use their lab facilities. Sequencing was performed by the  SNP&SEQ Technology Platform in Uppsala. The facility is part of the  National Genomics Infrastructure (NGI) Sweden and Science for life Laboratory. The  SNP&SEQ Platform  is also supported by the  Swedish Research Council  and the  Knut and Alice Wallenberg Foundation. Computations were performed on resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX).
Funding: This work was supported by the  Swedish Research Council , grant number  2016-05138  and  2012-03722  to AQ. CFM is funded by grants to  Hans Ellegren  from the  Swedish Research Council  ( 2013/08271 ) and  Knut and Alice Wallenberg Foundation. MS is funded by  IUF (Institut Universitaire de France)  and  ANR 21 T-ERC PLEIOTROPY  and received a stipend from  Wenner-Gren  for a sabbatical at  Uppsala University.
Search and review process Works addressing the aforementioned fields and tasks can be found in a variety of different scientific communities, particularly NLP, Web Mining, Information Retrieval (IR), Knowledge-based Systems and Artificial Intelligence (AI). Based on an initial set of publications from these communities dealing with extraction, verification or linking of claims and facts, found using a keyword-based search, we selected venues from the most relevant papers for systematic screening. Table  1  gives an overview of the chosen core journals, conferences, workshops and events. For each of those, we screened the proceedings of the years 2015-2019 (incl. 2020 and 2021 to the extent possible at the time of writing and revision preparation) and widened the search beyond these venues using online search engines and databases, also considering pre-prints.
g. , expressing extreme emotions); (iii) Propagation-based methods that are based on online spreading patterns; and (iv) Source-based methods investigating the credibility of sources. Rumours are often seen as a specific kind of fake news. Zubiaga et al. [224]  provide a survey on rumour identification and resolution, where conflicting and diverging definitions of rumours from related works are discussed, but without making parallels to related notions such as fake news or biased discourse. The main motivations are the assumed impact of social media on rumour generation and spread. The survey focuses on datasets for rumour detection, as well as existing tools for accessing, collecting and annotating social media data for the purposes of automated rumour detection. The authors analyse generic rumour detection systems by breaking them down to their different components and subsequently discussing the related approaches to address the challenges related to each of those components.
Claims in information retrieval and question answering In the area of Information Retrieval and Question Answering, several works focus on retrieving scientific claims and claims in digital libraries. Here, a claim is defined as a statement formulating a problem together with a concrete solution  [56]  or a sentence in a scientific document that relates two entities given in a query  [57, 58]. More generally, from a database-centric perspective, Wu et al. [207, 208]  represent a claim as a "parametrized query over a database". This allows to computationally study the impact of modifying a claim (i. e. its parameters) on the result of the query and to thus identify claim properties, such as claim robustness which may serve as evidence to detect potential misleadingness i. e. due to cherry-picking.
A recent work by Schiller et al. [158]  details the different and varying task definitions found in previous works, diverging not only with regard to domains, but also classes and number and type of inputs, and introduce a benchmark for stance detection that allows the comparison of models against a variety of heterogeneous datasets. In contrast to the works on viewpoint extraction described previously, works on stance detection focus more on supervised models and textual features (like the sentiment expressed in the text, or the use of polarised words), and less on the structure of the underlying network of users or documents, which can be exploited by unsupervised approaches. For two recent surveys of stance detection works, we refer to Küçük and Can  [91]  and Ghosh et al. [54]. In recent work, Sen et al.
[160]  compare untargeted and targeted opinion mining methods (sentiment analysis, aspect-based sentiment analysis, stance detection) to infer approval of political actors in tweets. They show that the compared targeted approaches have low generalizability on unseen and unfamiliar targets and that indirectly expressed stances are hard to detect, and thus identify the need for further research in this area. Chen et al. [29]  propose the task of substantiated perspective discovery where the goal is to discover a set of perspectives and supporting evidence paragraphs that take a stance to a given input claim, and release a first dataset for this task.
g. equivalence cliques between claims, keywords and topic concepts) and the generation of suitable ground truth data. Author identification and extraction Identifying the author of an utterance is not trivial  [11]  yet authorship is crucial for interpreting its meaning. Moreover, claims are often quoted by distant sources, e. g. in news articles or other media. The attribution of content to an author  25  is consequently gaining increased attention in the context of the analysis of news articles, e. g. by Newell et al. and Salway et al. [121, 155]  who build structured databases of claims with extracted quotes and author information. Approaches for quotation extraction and attribution from newspaper articles for both direct and indirect speech usually comprise three different component identification steps: (1) cue phrases signalling the presence of a quotation (e.
Claim verification A number of terms, such as fact-checking, truth discovery, claim or fact verification pertain to a large degree to the process of the automatic assignment of a veracity score to a statement uttered by a particular person or a group of people  [180]. Note that the analysis of false or mis-information spread, or fake-news detection, 26 defined and surveyed in Sharma et al. [163] , often deal with entire news articles or outlets and are, therefore, broader problems where claim verification can be seen as one of their ingredients. Claim truthfulness verification is reviewed in Cazalens et al. and Thorne et al. [26, 180] , where  [180]  in particular propose to unify diverging definitions of the task and its components from various disciplines, such as NLP, machine C O R R E C T E D P R O O F learning, knowledge representation, databases, and journalism.
Indeed, most of the existing techniques rely on background knowledge sources (e. g. encyclopedic knowledge graphs, such as DBpedia or Freebase) that provide a "truthfulness context"  [78, 179]  and a combination of various computational methods in order to infer the veracity scores of a claim either from those background knowledge sources or, more rarely, in a self-dependent manner. In addition, versatile features pertaining to all three main components of our model (meaning, utterance and context) are often considered in a combined manner, making it difficult to break down claim verification approaches along each of these three axes independently. In certain cases, claims are given a structured form (e. g. triples or database queries), which allows for the verification of entity-centric information by calling on machine learning techniques  [214].
Relatedness encloses various relationships, such as meronymy (a relation of composition (part-of) that is such that the meaning of a complex expression relates and can be expressed by the meanings of the parts from which it is constructed), antonymy (opposite meanings, including conflicting/contradicting claims), logical or textual entailment  [34] , same topic, or any kind of functional relationships or relationships or frequent association. A survey of semantic relatedness methods, evaluation and datasets is given by Zhang et al. [219]  and Hadj-Taieb et al. [69]. As opposed to logical entailment, textual entailment is understood as a relationship between pairs of text fragments where one entails the other if a human reading the former would be able to infer that the latter holds.
Relating utterances Several works address finding equivalent claims in the context of claim verification  [76, 78, 109] , where a claim matcher (or linker) is a component of a fact-checking system matching new claims to claims that have already been checked. Shaar et al. [162]  recently proposed the task of detecting previously fact-checked claims defined as ranking a set of verified claims according to their potential to help verify an input claim. They propose a learning-to-rank approach and release a first dataset for the task. Clustering similar arguments is at the core of the work by Reimers et al. [148]  who use contextualized word embeddings to classify arguments as pro or con and identify arguments that address the same aspect of a topic. Concerning the matching of text fragments more generally, recent advances in neural NLP and the advent of deep contextualized language models for language understanding, have allowed a renewal state-of-the-art techniques for matching text fragments through the pooling or aggregation of classical  [132]  and contextualized word-embeddings  [81, 105]  into phrase, sentence or document embeddings  [4, 5]  and the computation of distance metrics to find the closest matching utterances.
Generally speaking, considering the wide variety of methods and datasets involving claims and related notions, adopting a shared and well-defined vocabulary has the potential to significantly increase impact and reuse of research methods and data. C O R R E C T E D P R O O F C O R R E C T E D P R O O F C O R R E C T E D P R O O F C O R R E C T E D P R O O F C O R R E C T E D P R O O F C O R R E C T E D P R O O F C O R R E C T E D P R O O F C O R R E C T E D P R O O F C O R R E C T E D P R O O F C O R R E C T E D P R O O F C O R R E C T E D P R O O F.
Although the topographic evolution and erosion dynamics of the Himalayan range have been extensively documented, it is not known how the very high Himalayan peaks erode. Some conceptual models assume that intense periglacial processes involve regressive erosion of high peak headwall at rates dictated by valley-oor downcutting of glaciers. However, recent data indicate that frost-cracking intensity decreases with elevation, suggesting instead that highest Himalayan peaks are free of erosion, raising the question of their long-term evolution. Here, we report geological evidence for a giant rockslide that occurred around 1190 AD in the Annapurna Massif (central Nepal), involving a total rock volume of ~23km3, and that decapitated a paleosummit culminating most probably above 8000m of altitude.
The giant rockslide deposit from the Sabche cirque to the upper Seti valley The Annapurna massif displays a spectacular 6000m-high topographic step that separates the High Himalaya (HH) peaks from the Lesser Himalayan (LH) hills. The area presents steep slopes, active microseismic activity  23, 24  , strong ground shaking by major earthquakes  25, 26  (Mw≥8), and is characterized by heavy monsoon precipitation on its southern slopes. This tectonic and climatic setting induces some of the highest regional erosion rates in the Himalayan range, with values of 3-5 mm/yr  27- 29. The Annapurna massif, dominated by calcareous metasediments of the Tethyan Sedimentary Series  30, 31  (TSS) (Extended Data Fig. 1 ), is punctuated by several large glacial cirques, separated by high glaciated peaks (Fig.
Reconstruction of the collapsed paleo-summit In order to understand the origin of the collapsed rock mass, we rst identi ed the failure surface in the NE cirque part below the Annapurna IV (Figs. 1  and 3 ). There, the morphology of the rock cliffs stands out (see Method and Extended Data g. 6d) with fresh planar or prismatic structural surfaces, devoid of any major glacier nor runoff-related erosion. The giant rockslide, collapsing along this uneroded failure surface would have therefore beheaded the paleo-summit of Annapurna IV. To reconstruct the initial geometry of the paleo-Annapurna IV from the restored rock volume of 23 ±3km  3  , we hypothesized that the stability of the paleo-summit hillslopes follows mechanical characteristics that prevail for the whole Annapurna massif, for similar altitudes and lithologies.
We estimated the apparent cohesion of the entire massif (Method and Extended Data g. 7 & 8) of 1-2MPa to ensure the stability of 82-100% of the Annapurna high-elevation hillslopes. Afterward, we generated 1000's of random possible topographies for the Annapurna IV paleo-summit, and considered as acceptable only the output topographies that t both the collapsed rock volume and these regional hillslopes stability (Method and Extended Data Fig. 9 ). Through this Bayesian approach, we estimate that the paleo-Annapurna IV may have peaked at an average elevation of ~8100m and exceeded 8000m with a probability of 0. 65-0. 76 (Fig. 3d ).
Regarding the causes of the Sabche collapse, glacial valley deepening due to sub-glacial carving, combined to oversteepened hillslopes induced by frost cracking near the ELA (Scheller et al. , 2014), represent one of the major pre-conditioning factors that produce steep unstable headwalls surrounding glacier cirques. Schistosity orientation and structural plans seem also to be greatly guiding slope failure of the Annapurna IV (Extended Data g. 6d). The triggering of the rockslide remains by contrast di cult to identify. It has been suggested previously  38  that the lling of the Pokhara Basin was achieved by various pulses of debris ows following numerous landslides activated by 3 large earthquakes, at 1100 ±50AD  25  , 1255AD and 1344 AD 26. According to  14  C dating of Karuwa breccia (Fig.
In the southern part of the rockslide deposit, toward its base, along one of the internal shear zones, we collected three samples from two points ~100m apart showing less developed shear and gouge gures at the rst site (CA-13-273 and -274) than at the second (CA-13-283) (Extended Data g. 4). Collected samples measured 15x12x7cm and 7x7x5cm respectively. Sample preparation followed the procedure described in ref. 62 to isolate K-feldspar fraction. Bulk samples were sent to Actlabs or SARM-CRPG for major and trace element analysis, and used to estimate (see Supp. Info. SI-8 for full details) the total dose rate with the online Dose Rate and Age Calculator 63 (DRAC). Luminescence measurements have been performed at the University of Lausanne (Switzerland) using a post-IR IRSL protocol  64  to derive two IRSL signals measured at 50°C (IR50) and 225°C (pIR225).
Protocol details  62  are fully described in the Supp. Info. SI-8. Dose-equivalent distributions were statistically analysed65 using the Central Age Model (CAM) for all samples, and also the Minimum Age Model (MAM) for sample CA-13-283 which best represent luminescence reset during shearing (Extended Data Fig. 4 ). Final age estimates were calculated with individual fading corrections  66. Full analytical details, data and calculations are provided in Supp. Info. SI-8.
In order to calculate the spatial distribution of the FoS at the scale of the Annapurna region, we have developed a Matlab code largely adapted from previous approach  70. The slope stability analysis is based on the limit equilibrium concept for a 3D model with rotational movement considered on potential slip surface, whose geometry follows a surface portion of ellipsoid. Our model evaluates at each point of a topographic grid the slope stability conditions for a large number of randomly selected ellipsoidal slip surfaces (Extended data g. In contrast with previous model  70  , and because soil is absent above 4500m, we don't consider potential slip at the interface between soil and substrate, but a homogeneous substrate. Because we are not interested by exact friction and cohesion values but only by apparent regional values that could be applied to the slopes of the Annapurna IV, we don't consider either seepage forces, nor pore pressure effects.
Before applying our code to the Annapurna range, we tested on a benchmark case and ensured that the searching procedure was nding: 1. similar solution (FoS value) as previous studies  69, 70  ; 2. optimal ellipsoidal failure geometries not too far from the semi-analytic circular  72  or log-spiral  73  solutions. Regarding this later point, our optimal surfaces are a bit more concave than the semi-analytic solutions, but the FoS values equal to within <10%. To explore mechanic stability of the Annapurna range, we consider the steep areas within similar range of elevation and similar lithology as the Annapurna IV collapse, i. e. for elevation higher than ~4000m and for TSS units, excluding the at cirque bottom (Extended Data g. 8). For the calculation of the FoS, a xed value of the internal friction angle, set at j=35° (ref.