dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, we find a reduction of the afterslip at the bottom edges of the high angle faults (Section S8) that is likely compensated by the slip accommodated at the bottom edge of the shear zone itself (Figures  9e  and 9f ). We can compare Figure  9   2020 ), and by the smoothing method adopted during the inversion. Figure  9b  shows that the northern slipping area on the Campotosto fault overlaps with the co-seismic slip distribution of  Cheloni, D'Agostino, et al. (2019). However, their solution is derived from InSAR data that encompassed the displacement of the first month after the January 2017 mainshocks. We estimate that in that time period (i. e. , up to February 11) about 8% of the moment released is due to post-seismic relaxation. For what concerns the shear zone, it accommodates a few cm of slip on the eastern, slightly E-dipping, side (Figure  9f ).
In particular, we deem the relaxation of the lower crust to be a contributing mechanism in the 2 years following the Amatrice-Visso-Norcia seismic sequence. Bearing in mind the limits of the data and of our interpretation, we propose an afterslip model that is consistent with the co-seismic ruptures on the structures involved in the 2016-2017 seismic sequence. Furthermore, we provide some preliminary values of the viscosity and thickness of the lower crust, leaving further investigations to future studies, which might consider an afterslip + viscoelastic joint inversion of possibly longer time series.
Data Availability Statement We use publicly available raw GPS data. Raw GPS displacement time series are available on https://doi. org/10. 5281/zenodo. 4633475; the GPS displacement time series used in input to vbICA are available on https://doi. org/10. 5281/zenodo. 4633412. The seismic catalog is taken from  Michele et al. (2020)  and available at https://doi. org/10. 5281/zenodo. 3712731. The precipitation, temperature, and river flow data used to implement the hydrological model are available at https://annali. regione. umbria. it/, http://www. sir. toscana. it/consistenza-rete, https://console. regione. marche. it/ (http://app. protezionecivile. marche. it/sol/ indexjs. sol?lang=it), for the Umbria, Toscana, and Marche region, respectively. Data for the Abruzzo region are available under request to the REGIONE ABRUZZO "Dipartimento delle Opere Pubbliche, Governo del Territorio e Politiche Ambientali Servizio Programmazione Attività di Protezione Civile Ufficio Idrografico e Mareografico" (idrografico@regione.
abruzzo. it). Extraterrestrial irradiance data are available at http:// www. soda-pro. com/web-services/radiation/extraterrestrial-irradiance-and-toa. Drainage direction maps used to define river basins are available at www. hydrosheds. org/page/availability. Global data sets used for the hydrological load model are taken from https://grace. jpl. nasa. gov/data/get-data/jpl_global_mascons/ (GRACE), https://disc. gsfc. nasa. gov/datasets/GLDAS_NOAH025_3H_2. 1/summary (GLDAS); http://loading. u-strasbg. fr (ERA-interim model); mean monthly temperature: GHCN Gridded data provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web site at https://psl. noaa. gov/. A version of.
Filliettaz proposed to identify and discuss some of the challenges that apprentices from different cultural backgrounds are facing when entering the workplace and when applying new knowledge and skills in professional settings. He provided empirical xv Preface material for addressing the issue of discourse and interaction in the workplace and discussed some practical implications regarding the role of language and interactional competences in initial vocational education programmes. Professor Greg Kessler, from Ohio University (USA), dealt with disruptive CALL: anticipated innovations in technology, pedagogy, and language learning. The presenter shared his thoughts about the changing nature of language teaching and learning in relation to technological and pedagogical innovations, including emerging trends in artificial intelligence, automation, assessment, data driven learning, automated speech interfaces, and a variety of forms of natural language processing and artificial intelligence.
Results Responses were logged for 130 subjects and collated using a script. There were large differences between languages, between responses for sentences and words, and between native and non-native judgments. Table  1 , Table  2 , Table  3 , and Table  4  show results for the portions of the data we considered most informative. Full details are posted in the supplementary materials. 4 15. 6 11. 8 30. 4  2. 9 0. 0 0. 0 (same)  (15. 3) (35. 9) (17. 7) (56. 8) (24. 4) (55. 0) (39. 1) (50. 0) (0. 0) (14. 3).
Method This study collected quantitative and qualitative data via online surveys. Student data were gathered over two weeks using a combination of 25 closed and openended questions. Survey questions were generated in reference to studies by  Niño (2009 ), Clifford et al. (2013) ,  Briggs (2018), and Tsai (2020). One hundred and fifty-nine university students taking Chinese, French, German, Japanese, and Spanish at beginner, intermediate, and advanced levels participated in this study. Twelve instructors (representing all languages and levels) responded to a 16-question survey. STATA was used for the statistical analysis of the quantitative data and NVivo for the thematic analysis of the qualitative data. This article provides a preliminary analysis of the data, which will be explored in more detail in a following paper.
OTs for L2 writing Only a few teachers provided suggestions on how their students might use OTs for L2 writing. They assumed that students were using OTs to do the work for them, "to get the assignment done quickly and with few errors and presenting it as their own" or by "writing first in L1 and then translate it into L2 to be used as assignment". Such assumptions reflect the widely held belief that OTs are used for cheating and are detrimental to language learning (Correa, 2014). One teacher speculated that students use OTs "to check and correct entire clauses they have first written themselves". The analysis of the qualitative data indicates that students are using OTs to support their writing in a variety of ways, at different stages in the writing process, mainly: • as dictionaries: aware of the limitations of OTs for longer text passages, most students (87%) stated that they used OTs primarily for looking up words, spell checking, and improving a text stylistically by looking up synonyms and idioms; • as grammar checkers: students identified a range of OT capabilities to deal with grammatical uncertainties while writing, such as syntax, verb conjugation, gender, and adjective agreement; and • for proofreading: as one of the teachers suggested, students enter their L2 text into OTs to either confirm accuracy or get alternative suggestions.
An online survey was used for collecting data from volunteering students of the L2 Icelandic course. It consisted of 72 questions to collect quantitative data, six of these questions were about the participants' background, and 65 were Likert-scale questions on ten different topics concerning the online classes, and one open-ended question for collecting qualitative data on students' suggestions or further remarks. The questions were tailor-made for the actual course and mode of teaching in order to capture a realistic picture of the situation. The questions in the survey came with a Likert-scale of six options ranging from 'strongly disagree' to 'strongly agree'. These survey questions were constructed around ten different themes that guided the research questions of this study. Results summarising these ten themes are presented in the next section.
Regarding using other online tools during homework assignments (  8 ), 46% used online dictionaries for Icelandic (Glosbe, Snara), 58% used the DIM -The Database of Icelandic Morphology website -53% used Google Translate, and 27% reported using other online tools. Regarding their satisfaction with the course curriculum (  9 ), 73% agreed that it was challenging and 46% were satisfied with it. Regarding their overall satisfaction with the online format of the course (10), 46% were satisfied with it. In the open-ended question, students reported that the course felt fast and that practising the language outside of the class with local speakers was restricted due to the COVID-19 regulations on gatherings. Although the class was divided into two groups with different levels, beginner and intermediate, some students still felt that their group could have been divided into two further smaller groups with different language levels.
Similarly, while some Icelandic families relocate abroad permanently, others intend to return to Iceland, and their children need to continue practising Icelandic L2 at different levels. In both cases, lack of language exposure remains an issue. The amount of input can therefore vary, which affects the length of time needed for learning and acquiring Icelandic L2  (Thordardottir & Juliusdottir, 2013, p. 412 ). Children's requirements and needs may vary depending on age, interests, parental support, family arrangements, and not least of all the availability and accessibility of language materials and tools. A wide variety of learning materials in Icelandic L2 for children is already available online, though parents and guardians are often unaware of how to access them. This article presents two parallel studies: a qualitative investigation of families' experiences using such material and their learning goals for their children, and the creation of a comprehensive central database of currently available online/electronic Icelandic L2 material for children.
g. frequency of purposeful study, type and frequency of materials used, and language skill areas emphasised); and further information pertaining to families' experience of online L2 material (e. g. ease of locating and using material, children's interest in material, materials that parents would like to see available in the future). The parallel desktop research involved locating and analysing all relevant online materials for children learning Icelandic L2. Keywords 'Icelandic as a second language', 'children at primary school level', and 'learning materials for children at primary school level' were used respectively in Google Search until no new resources were presented. Each resource was inspected using content analysis  (Cohen, Manion, & Morrison, 2011). A central database was created with a general overview of each resource provider, providing a brief description of materials available.
From the output of this game, i. e. the labelled sentences, corpora will be compiled that can be used by teachers, material developers, and lexicographers. The game development has three stages. In the first stage, the datasets for the game have been prepared. In order to create datasets of potentially good example candidates and 'bad' example candidates, the web corpora have been automatically filtered for each language with some common and some language-related heuristics using the GDEX function 12 in the Sketch Engine. The second stage involves game preparation, i. e. the development of varied game modes, implementation of gamification aspects such as scoring and motivation, and the design of the interface. As part of future work, the third stage will be concerned with training of machine learning models for each language based on the identification and categorisation of problematic content by the players.
Acknowledgments We would like to thank  Hossein, Matilde , and  Rebe  for recording the French, Italian, and Spanish audio respectively. Acknowledgments We would like to thank all those who participated in the DEC at University Paris 8. Acknowledgements We would like to thank  Elina Vilar  ( Queen Mary ) and  Sabela Melchor-Couto  for their collaboration. Acknowledgments This research was supported by the research fund ( 2021Y041 ).
Acknowledgements We would like to thank  Maureen Ehrensberger-Dow  for her valuable contribution to this short paper, as well as swissuniversities and  ZHAW Digital , the  University of Neuchâtel , and  Bern University of Applied Sciences for co -funding the project. Acknowledgements This study belongs to a research project financed by  UPV  ( PAID-01-19 ), resulting in a doctoral dissertation. Acknowledgements Gratitude is expressed to the  Icelandic Centre for Research RANNÍS  for funding this study, and to Tungumálatöfrar, The  Árni Magnússon Institute for Icelandic Studies and Móðurmál  for their collaboration. Acknowledgments This work was supported by  JSPS KAKEN  Grant No. 20K00785  (Principal Investigator:  Atsushi Iino ;  Co-Investigator: Brian Wistner ). Acknowledgements This research has been co-funded by the  European Union  under the  ERASMUS+ programme, with Centre for Technology-Enhanced Learning, Lancaster University (UK); Language Centre, University of Cyprus (Cyprus); Institute for Learning Innovation, Friedrich-Alexander University (Germany ); and  AKTO Art and Design College (Greece).
Acknowledgments We would like to thank  Dr Heike Neumann  and  Nina Padden at Concordia's ESL Center  for their assistance and support. Acknowledgements I would like to thank  Dr Julian Lewis  for his advice on my paper. Acknowledgements We would like to thank  Open Learning  Project students from  UCA , and French students and staff from the  Department of Modern Languages  and Literatures from the  UWI, Mona  for their participation. Acknowledgements We would like to thank  Open Learning  Project students from  UCA , and French students and staff from the  Department of Modern Languages  and Literatures from the  UWI, Mona , for their participation. Acknowledgments This research was supported by  KAKENHI   Grant-in-Aid for Scientific Research  (A), Grant No. 19H01120. Acknowledgements This research project is supported by a  JSPS KAKENHI  ( Grants-in-aid for Scientific Research ), Grant number  19K00826 , ' Developing a diagnostic language assessment of Japanese EFL  learners' English email writing'.
Acknowledgments This study is funded by the  China Scholarship Council. The authors also wish to thank all participants for their time and support. Acknowledgements The author welcomes corrections, particularly if a language is missing from Table 1. Acknowledgements This project is part of the 'Qualitätsoffensive Lehrerbildung', a joint initiative of the  Federal Government  and the Länder which aims to improve the quality of teacher training. The programme is funded by the  Federal Ministry of Education and Research. The authors are responsible for the content of this publication. Acknowledgments We thank the students  Corentin Leroyer  ( 2019 ),  Viet Minh Thong Le  ( 2020 ), and  Dinh Triem Phan  ( 2020 ) for their contribution to the statistical analysis of the data. Acknowledgements The authors have been funded by the  Horizon   2020 Framework Programme  of the  European Union  under the enetCollect  CA16105   COST Action , the  Slovenian Research Agency  (research core funding No.
Scite shows how a citation was used by displaying the surrounding textual context from the citing paper and a classification from our deep learning model that indicates whether the statement provides supporting or contrasting evidence for a referenced work, or simply mentions it. Scite has been developed by analyzing over 25 million full-text scientific articles and currently has a database of more than 880 million classified citation statements. Here we describe how scite works and how it can be used to further research and research evaluation.
Because of the lack of context and limited metadata available beyond paper titles, authors, and the date of publications, it has only been possible to calculate how many times a work has been cited, not analyze broadly how it has been cited. This is problematic given citations' central role in the evaluation of research. In short, not all citations are made equally, yet we have been limited to treating them as such. Here we describe scite (scite. ai), a new citation index and tool that takes advantage of recent advances in artificial intelligence to produce "Smart Citations. " Smart Citations reveal how a scientific paper has been cited by providing the context of the citation and a classification system describing whether it provides supporting or contrasting evidence for the cited claim, or if it just mentions it.
) , the citation type indicating intent (supporting, contrasting, or mentioning), and editorial information from Crossref and PubMed, such as corrections and whether the article has been retracted (Figure  1 ). Scite previously relied on Retraction Watch data but moved away from this due to licensing issues. Going forward, scite will use its own approach 1  to retraction detection, as well as data from Crossref and PubMed. Adding such information to citation indices has been proposed before. In 1964, Garfield described an "intelligent machine" to produce "citation markers," such as "critique" or, jokingly, "calamity for mankind"  (Garfield, 1964). Citation types describing various uses of citations have been systematically described by Peroni and Shotton in CiTO, the Citation Typing Ontology  (Peroni & Shotton, 2012).
PLOS Labs attempted to enhance citation analysis with the introduction of "rich citations," which included various additional features to traditional citations such as retraction information and where the citation appeared in the citing paper  (PLOS, 2015). However, the project seemed to be mostly a proof of principle, and work on rich citations stopped in 2015, although it is unclear why. Possible reasons that the project did not mature reflect the challenges of accessing the literature at scale, finding a suitable business model for the application, and classifying citation types with the necessary precision and recall for it to be accepted by users. It is only recently that machine learning techniques have evolved to make this task possible, as we demonstrate here. Additional resources, such as the Colil Database  (Fujiwara & Yamamoto, 2015)  and SciRide Finder  (Volanakis & Krawczyk, 2018)  both allow users to see the citation context from open access articles indexed in PubMed Central.
Overview Smart citations are created by extracting and analyzing citation statements from full-text scientific articles. This process is broken into four major steps (see Figure  2 ): 1. The retrieval of scientific articles 2. The identification and matching of in-text citations and references within a scientific article 3. The matching of references against a bibliographic database Figure  2. The scite ingestion process. Documents are retrieved from the internet, as well as being received through file transfers directly from publishers and other aggregators. They are then processed to identify citations, which are then tied to items in a paper's reference list. Those citations are then verified, and the information is inserted into scite's database. 4. The classification of the citation statements into citation types using deep learning.
Identification of In-Text Citations and References from PDF and XML Documents A large majority of scientific articles are only available as PDF files 2  , a format designed for visual layout and printing, not text-mining. To match and extract citation statements from PDFs with high fidelity, an automated process for converting PDF files into reliable structured content is required. Such conversion is challenging, as it requires identifying in-text citations (the numerical or textual callouts that refer to a particular item in the reference list), identifying and parsing the full bibliographical references in the reference list, linking in-text citations to the correct items in this list, and linking these items to their digital object identifiers (DOIs) in a bibliographic database. As our goal is to eventually process all scientific documents, this process must be scalable and affordable.
To accomplish this, we utilize GROBID, an opensource PDF-to-XML converter tool for scientific literature  (Lopez, 2020a). The goal of GROBID is to automatically convert scholarly PDFs into structured XML representations suitable for large-scale analysis. The structuration process is realized by a cascade of supervised machine learning models. The tool is highly scalable (around five PDF documents per second on a fourcore server), is robust, and includes a production-level web API, a Docker image, and benchmarking facilities. GROBID is used by many large scientific information service providers, such as ResearchGate, CERN, and the Internet Archive to support their ingestion and document workflows  (Lopez, 2020a). The tool is also used for creating machine-friendly data sets of research papers, for instance, the recent CORD-19 data set  (Wang, Lo et al.
Matching References Against the Bibliographic Database Crossref Once we have identified and matched the in-text citation to an item in a paper's reference list, this information must be validated. We use an open-source tool, biblio-glutton  (Lopez, 2020c) , which takes a raw bibliographical reference, as well as optionally parsed fields (title, author names, etc. ) and matches it against the Crossref database-widely regarded as the industry standard source of ground truth for scholarly publications 3. The matching accuracy of a raw citation reaches an F-score of 95. 4 on a set of 17,015 raw references associated with a DOI, extracted from a data set of 1,943 PMC articles 4  compiled by  Constantin (2014). In an end-toend perspective, still based on an evaluation with the corpus of 1,943 PMC articles, combining GROBID PDF extraction of citations and bibliographical references with biblio-glutton validations, the pipeline successfully associates around 70% of citation contexts to cited papers with correctly identified DOIs in a given PDF file.
Task Modeling and Training Data Extracted citation statements are classified into supporting, contrasting, or mentioning, to identify studies that have tested the claim and to evaluate how a scientific claim has been evaluated in the literature by subsequent research. We emphasize that scite is not doing sentiment analysis. In natural language processing, sentiment analysis is the study of affective and subjective statements. The most common affective state considered in sentiment analysis is a mere polar view from positive sentiment to negative sentiment, which appeared to be particularly useful in business applications (e. g. , product reviews and movie reviews). Following this approach, a subjective polarity can be associated with a citation to try to capture an opinion about the cited paper.
The evidence used for sentiment classification relies on the presence of affective words in the citation context, with an associated polarity score capturing the strength of the affective state  (Athar, 2014; Halevi & Schimming, 2018; Hassan, Imran et al. , 2018; Yousif et al. , 2019). Yan et al. (2020) , for instance, use a generic method called SenticNet to identify sentiments in citation contexts extracted from PubMed Central XML files, without particular customization to the scientific domain (only a preprocessing to remove the technical terms from the citation contexts is applied). SenticNet uses a polarity measure associated with 200,000 natural language concepts, propagated to the words and multiword terms realizing these concepts. In contrast, scite focuses on the authors' reasons for citing a paper.