dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank  Thomas Lecocq  and an anonymous reviewer for their comments which helped to improve the manuscript. Pauchet H. , Rigo A. , Rivera L. , Souriau A. , (1999) A detailed analysis of the February 1996 aftershock sequence in the eastern Pyrenees, France, Geophys. J. Int. , 137,  107-127. Provost, L. , Scotti, O. , 2020. QUake-MD:  Open-Source Code to Quantify Uncertainties in Magnitude-Depth Estimates of Earthquakes from Macroseismic Intensities, Seismological Research Letters ,  91 ,  2520-2530. Renouard, A. , Grunberg, M. , Doubre,  C. Maggi , A. , 2019. Automatic detection and machine learning-based discrimination of earthquakes in northwestern intraplate Europe using SeisComP3 and the  AlpArray network. Geophysical Research Abstracts Vol. 21, p1-1.
1p. RESIF. (1995a). RESIF-RLBP French Broad-band network, RESIF-RAP strong motion network and other seismic stations in metropolitan France  [Data set]. RESIF -Réseau Sismologique et géodésique Français. https://doi. org/10. 15778/RESIF. FR RESIF. (1995b). RESIF-RAP  French Accelerometric Network  [Data set]. RESIF -Réseau Sismologique et géodésique Français. https://doi. org/10. 15778/RESIF. RA RESIF. (2018). CEA/DASE broad-band permanent network in metropolitan France [Data set]. RESIF -Réseau Sismologique et géodésique Français. https://doi. org/10. 15778/RESIF. RD Rey, J. , Tinard, P. , 2015. Estimation financière du risque sismique à l'échelle départementale: à l'interface entre sismologie et réassurance, travaux communs  CCR-BRGM  ( 2014-2019 ).
The database of historical macroseismic intensity SISFRANCE can be found at https://sisfrance. irsn. fr, and data can also be found at AHEAD European Historical macroseimic database (https://www. emidius. eu/AHEAD/). Shakemaps based on instrumental and microseismic data are available at http://www. franceseisme. fr. The Website of RESIF is http://www. resif. fr. Facebook and Twitter social networks (@franceseisme) are https://www. facebook. com/franceseisme and https://twitter. com/FranceSeisme. Two European projects are cited : SISPYR: http://www. sispyr. eu and TURNkey: https://earthquake-turnkey. eu/.
, 2014). Data records that suggest the presence of a VME frequently have varying degrees of uncertainty due to limited spatial and temporal sampling effort in the deep-sea regions where they are most found, e. g. Morato et al. (2018). Moreover, absence data, i. e. samples where no VMEs have been identified, are less regularly collated in databases of deep-sea biodiversity. These limitations have important consequences and implications for managers designing protective measures to achieve the objectives of the above UNGA resolutions (Figure  1 ). Managers may choose a high level of precaution and close all areas to bottom-contact fishing where VMEs are known or likely to occur, based on the best available scientific information. Yet, some of these areas, or parts thereof, may not host an actual VME (Type 1 error) and this may cause unnecessary fisheries restrictions with resultant socio-economic impacts.
Data on VMEs A central database holding information on the distribution and abundance of habitats and species considered to be indicators of VMEs across the North Atlantic, is maintained by the ICES Data Centre, in collaboration with the Joint ICES/NAFO Working Group on Deep-water Ecology (WGDEC; ICES, 2020b). This database aims to store and make available all known VME habitat and indicator records in the North Atlantic, covering deep water areas inside and outside national jurisdiction. For the development of closed area options, only data from this database (accessed May 2020) were used directly in the identification of VMEs, to ensure comparability between regions and same quality assumptions. The definition of VMEs for submission to the database are based on the five criteria defined by the FAO International Guidelines for the Management of Deep-sea Fisheries in the High Seas  (FAO, 2009) : (1) uniqueness or rarity, (2) functional significance of the habitat, (3) fragility, (4) life-history traits of the component species that make recovery difficult, and (5) structural complexity.
Each year, a data call is sent out to ICES member states requesting any new data on VMEs to be submitted to the VME database. The data call provides a list of habitats that are currently recognized as VMEs, as well as their representative taxa, i. e. VME indicator taxa  (ICES, 2020c). VME data submissions can take three forms: (1) VME habitats-records for which there is unequivocal evidence for a VME, e. g. Remotely Operated Vehicle observations of a cold-water coral reef; (2) VME indicators-records that suggest the presence of a VME with varying degrees of positional uncertainty, e. g. bycatch of gorgonian corals from a fishing vessel; and (3) absence datasamples where neither a VME nor a VME indicator have been identified. Absence data is rarely available in the VME database and thus not used in the current analysis.
In the VME database, VME habitat records are considered unequivocal VMEs, whereas VME indicator records represent data from multiple sources, collected at different times through different survey methods, including older data from scientific literature. VME indicator data are, therefore, not standardized and cannot easily be compared against each other. To use these data for understanding VME distribution in North Atlantic waters, while taking into account standardization issues, a multi-criterion assessment system was developed by WGDEC  (ICES, 2015)  and further refined by  Morato et al. (2018). A series of transparent steps are followed to produce a VME "likelihood" score and a confidence score. This method produces the "VME index," which indicates the likelihood of an area containing a VME, based on the underlying VME indicator data.
The index combines a ranked VME indicator "vulnerability score," based on expert knowledge of each indicator species considered against the five FAO criteria for VMEs  (FAO, 2009) , with available data on the abundance of VME indicator species records, where provided in the database. These two parameters are combined and the VME index scores overall VME likelihood as either high, medium, or low. The index is mapped on a spatial C-square grid scale  (Rees, 2003)  of 0. 05 • × 0. 05 • (approximately 3 km by 5 km at 62 • latitude, hereafter termed C-square). Records of VME habitat submitted to the database are assigned to a "VME habitat" category, and therefore, do not sit on the "likelihood" scale. Full details on the method for the VME index are provided in  Morato et al. (2018). The distribution of VME habitats and the VME index is illustrated in Supplementary Figure  S1.
Although the VME data are used at the C-square resolution to identify VME likelihood, the database holds records as point or line coordinates. Therefore, to assess how the different closure scenarios and options captured these records, we evaluated the closure both at the C-square and individual record level. Where the record geometry was recorded as a line, that is, if it arose from a trawl or a camera tow, the mid-point of the line was taken and treated as a point record.
Caveats and limitations of the VME database During the development of this work, some important data gaps in VME locations (known but not submitted to the ICES database) were identified and have been partially corrected (ICES, 2021a), although not in time to be included in this work, which is based on data availability in May 2020. Key gaps in VME locations in this work are mostly found in the Bay of Biscay and the Iberian Coast ecoregion and include: coral reefs, coral gardens and sponge aggregations associated with the Avilés canyon system  (Louzao et al. , 2010; Sánchez et al. , 2014)  and the Galicia Bank  (Somoza et al. , 2014; Altuna 2017; Serrano et al. , 2017) ; sponges and stony corals from Nazaré submarine canyon on the Portuguese margin  (Huvenne et al. , 2012)  as well as sea pen presence records  (Ruiz-Pico et al.
, 2017) , and numerous mud volcanoes in the Gulf of Cádiz  (Díaz del Río et al. , 2014; Urra et al. , 2021). There are also several improvements that could be made to the VME index to support further use for the identification of VME closure proposals  (ICES, 2018). The VME index is based on a mix of information on the presence of VME indicator groups, the characteristics of these species and measures of their abundance (where available), and the contribution of each of these elements is not detailed in the final index value. This means that it is difficult to infer what an index value within a specific location is likely to represent in terms of indicator type(s) and abundance. The EMODnetseafloor geology data layers were used to identify VME geophysical elements in the two ecoregions. These data layers are publicly available through the EMODnet Geology portal.
Data on fishing activity The fishing distribution in both ecoregions was estimated for mobile bottom-contacting gears (bottom otter trawls, bottom seines, dredges, and beam trawls) and static bottom-contacting gears (pots and traps, gillnets, and longlines). The fishing distribution was described by coupling vessel monitoring systems (VMS) data with logbook data via an annual ICES data call, see further ICES  (2019). VMS data products produced through the VMS and logbook data call are currently aggregated on a spatial grid scale of 0. 05 • × 0. 05 • (i. e. C-square), matching the resolution of the VME data. The Csquare cell size used for this aggregation is chosen as it represents an optimum solution given the current time interval between the polling frequency seen in the available VMS data (typically 1 h, but ranging between 15 min and 2 h) and the distance a vessel travelling at speeds consistent with fishing will cover during this period.
(2015)  and using expert input  (ICES, 2019). SAR is the sum of the swept area divided by the area of each grid cell per year. Therefore, the C-square SAR indicates the theoretical number of times the entire grid cell has been swept if effort were evenly distributed within the cell. In reality, fishing in a C-square is often spatially aggregated. These spatially aggregated patterns may either shift over time so that the long-term distribution becomes spatially random, or they remain consistent over time since part of the grid cell is untrawlable  (Ellis et al. , 2014). The uncertainty in the spatial distribution of fishing within a C-square has important implications for VME-fishing interactions as VMEs within a C-square may or may not spatially overlap with fishing (and this cannot be resolved with the spatial resolution of the data provided to ICES by member states).
An estimate of the value of landings is available in the VMS data call for each C-square but no estimate of variable cost is available. Since fishing effort and value of landings are correlated at large spatial scales (Pearson-product correlation coefficient is 0. 83 for the Celtic Sea ecoregion in the depth range of 400-800 m; no information on value is available for the Iberian Peninsula), we assumed they might equally well represent contribution margin. Nonetheless, some low fishing intensity areas may be associated with a high contribution margin and suggested closures in these areas may disproportionally impact local fisheries. While it is possible to quantify the intensity of mobile bottomcontacting gears using VMS data, issues still surround the use of VMS data to quantify intensity for vessels using static gears.
Celtic seas ecoregion In the Celtic Seas ecoregion, the ICES VME database contains 3091 records for VME habitats and 9278 records for VME indicators (as of May 2020). This information, from across the ecoregion, has been collected through various gear types and survey methods. Most records come from the northwest of Scotland and west of Ireland (Supplementary Figure  S1 ). The fishing footprint is extensive and covers 85% of the 400-800 m depth in the Celtic Seas ecoregion (Table  2 ). Along the continental slope, unfished C-squares are commonly found on the deeper edge of the bathymetric range (Figure  2 ). This is most apparent on the northern edge of the United Kingdom EEZ north of the Wyville-Thomson Ridge where just over half of the depth range appears to be fished moving from shallow into deeper waters.
Bay of biscay and the iberian coast ecoregion The Bay of Biscay and the Iberian Coast ecoregion covers the southeastern shelf seas of the EU. It includes all or parts of the EEZs of France, Spain, and Portugal, and a small proportion of the High Seas. In total, the ICES VME database holds 3834 records for VME habitats and indicators within this ecoregion (as of May 2020). The VME indicators are all ranked as Medium or Low VME index (Supplementary Figure  S1 ). These records are unevenly distributed across the EEZs of France, Spain, and Portugal, and are mainly associated with a few VME geophysical elements, including: the Me´riadzek Terrace, submarine canyons of the Bay of Biscay and Portuguese margins, El Cachucho (Le Danois Bank), the Gorringe Bank, as well as two mud volcano fields in the Gulf of Cádiz.
As noted in the section "Caveats and Limitations of the VME Database," the VME database is known to be missing VME data in this ecoregion. The fishing footprint is extensive and covers 83% of the 400-800 m depth in the ecoregion (Figure  2  and Table  6 ). Most of the unfished areas seem to be concentrated in Spanish waters (25% of Csquares unfished) followed by Portuguese waters (18% of C-squares unfished), whereas there are very few unfished areas in French waters (3%). Most of the unfished C-squares are concentrated in three areas: El Cachucho where fishing has been strictly limited, Galicia Bank and the Gulf of Cádiz, including both Spanish and Portuguese waters. However, caution is advised in assuming that all the unfished C-squares in this region are really unfished due to the omission of a large proportion of fishing effort of the Spanish fleet and the proximity of some of these deep-water areas to the coast, where they are accessible to smaller vessels without VMS.
 This means that full knowledge of the spatial distribution of the VMEs is not a pre-requisite for management action and that there is an expectation to accept Type 1 error over Type 2 error. The scenarios tested can be combined in other combinations to produce alternative options e. g. the C-squares identified under Scenario 1 option 2, which includes the VME geophysical elements, could be combined with those identified under Scenario 2 option 2, which includes all known VME data from the VME database, to further reduce Type 1 error. The scenarios tested can be updated when new information on VMEs and fishing activity becomes available.
Acknowledgements The manuscript has benefitted from discussions with  Raluca Ivanescu  and  Carolina Alibert-Deprez  from the  European Commission's Directorate-General for Maritime Affairs and Fisheries (DG MARE ). Maps of seabed habitats and geology have been derived from data that are made available under the European Marine Observation Data Network (EMODnet) Seabed Habitats project (http://www. emodnet-seabedhabitats. eu/) and  Geology  project (ht tp://www. emodnet-geology. eu),funded by  DG MARE. The output reflects only the authors' view, and the  European Union  cannot be held responsible for any use that may be made of the information contained therein. The authors are grateful for the comments of two reviewers to improve the manuscript.
Data availability The R-scripts which produced the closed area options and data summaries, including closure. shp files, are available on an open-source platform (WKEUVME GitHubsite). For each ecoregion/Management Option/Closed Area, there is a table of the coordinates available on GitHub that indicates the VME habitat, VME indicator and VME geophysical element data present. The GitHub also includes spatial information on the static, mobile and combined fishing footprints. VME habitat and indicator records that are publicly available can be downloaded from the ICES VME data portal.
In a study conducted on more than 10,000 SARS-CoV-2 genomes from four databases from patients in 68 countries, 5775 distinct genomes were identified, including 2969 missense mutations and 36 stop-gained variants  [14]. Investigation of a possible selective advantage or of an association with clinical severity of these variants is of paramount importance. Mutations in the gene encoding Spike protein of SARS-CoV-2 have been showed to affect both the virus infectivity and antigenicity in vitro  [15]. In Marseille, France, a two-act pattern of incidence of COVID-19 cases occurred and significant differences in clinical outcomes were observed between patients seen in March-April 2020 and those seen in June-August  [16, 17]. severe acute respiratory distress  [6]. Additionally, neurological symptoms including, notably, anosmia and ageusia are frequent  [6] , and some patients may also present with cutaneous  [7]  and gastrointestinal symptoms  [7].
V2 or B. 1. 351), Brazil (P1) and California, USA (B. 1. 427 and B. 1. 429), is causing concerns (Table  1 )  [12, 13]. In a study conducted on more than 10,000 SARS-CoV-2 genomes from four databases from patients in 68 countries, 5775 distinct genomes were identified, including 2969 missense mutations and 36 stop-gained variants  [14]. Investigation of a possible selective advantage or of an association with clinical severity of these variants is of paramount importance. Mutations in the gene encoding Spike protein of SARS-CoV-2 have been showed to affect both the virus infectivity and antigenicity in vitro  [15]. In Marseille, France, a two-act pattern of incidence of COVID-19 cases occurred and significant differences in clinical outcomes were observed between patients seen in March-April 2020 and those seen in June-August  [16, 17].
Search Strategy and Selection Criteria This review was conducted according to the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) guidelines (http://www. prisma-statement. org (accessed on 31 May 2021)). The following databases were investigated in an attempt to identify all relevant studies published on: PubMed (http://www. ncbi. nlm. nih. gov/ pubmed (accessed on 31 May 2021)), Web of Science (https://www. webofknowledge. com/) (accessed on 31 May 2021) and Google Scholar (http://scholar. google. fr/) (accessed on 31 May 2021). The most recent search was conducted on 31 May 2021. The topic search terms used for searching through the databases were the following: #1: "variant" OR "variation" OR "mutation"; #2: "SARS-CoV-2" OR "COVID-19"; #3: "severity" OR infectivity"; #4: #1 AND #2 AND #3.
Study Selection and Types of Studies The study selection is presented in the flow diagram (Figure  1 ). The search algorithm produced 1642 articles from the PubMed, Web of Science and Google Scholar databases. After removing duplicates, 758 articles were scanned, based on their titles and abstracts. A total of 254 articles were processed for full-text screening. Sixty-three articles met the inclusion criteria and were included in the qualitative synthesis of the systematic review (Figure  1 )  [8] [9] [10] [11]. Of the 63 publications included, five were non-peer reviewed preprints  [16, 17, 52, 54, 66]. Eleven articles reported in vitro, in silico or animal model studies  [15, 18, 19, [22] [23] [24] [25] [26] [27] [28] [29] ; nineteen articles reported clinical studies  [16, [37] [38] [39] [41] [42] [43] [44] [45] [46] [47] [48] [49] 51, [70] [71] [72] [73].
The remaining 33 articles analyzed SARS-CoV-2 genomes downloaded from the GISAID or other available databases with patient status  [8] [9] [10] [11] 20, 21, [30] [31] [32] [33] [34] [35] [36] 40, 50, [52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69]. Most clinical studies were conducted before July 2020, corresponding to the first phase of the COVID-19 pandemic. Only four studies were conducted during two different episodes of the pandemic (from March to May and May to July 2020 in one study, from March to November in two studies and from March 2020 to January 2021 in one study)  [41, 46, 70]. Most of the studies were conducted in the UK (9), followed by France (6), the USA (6), China (2), Singapore (2), Brazil (1), UAE (1), Vietnam (1) and Uruguay (1).
Relation between Viral Mutation and Infectivity of SARS-CoV-2 Twenty-five studies reported the relation between viral mutations and virus load or infectivity of SARS-CoV-2 (Table  2 ), of which five articles (two in vitro/animal model studies  [18, 19] , two studies based on SARS-CoV-2 genomes downloaded from electronic databases or community-based testing datasets (bigdata analysis)  [20, 21]  and one clinical study  [16] ) showed no impact of mutations with infectivity of SARS-CoV-2. The remaining 20 studies (nine in vitro/animal model or in silico studies  [15, [22] [23] [24] [25] [26] [27] [28] [29] , eight bigdata analyses  [9, [30] [31] [32] [33] [34] [35] [36]  and three clinical studies  [37] [38] [39] ) showed that SARS-CoV-2 variants were significantly associated with increase in infectivity.
These variants were significantly associated with an increase in COVID-19 mortality  [55]. By analyzing the data from datasets provided by Public Health England, Davies et al. showed that the 501Y variant was associated with a 12-64% higher risk of death  [63]. Stauft et al. conducted an animal model study to evaluate the pathogenicity of D614 and G614 variants in the Spike protein. The authors showed that there was no difference in pathology in lung tissues of hamsters infected with these variants  [18]. A total of ten studies reported the relation between new VOCs and severity of COVID-19, of which three studies (two bigdata analyses and one clinical study) showed negative associations. Most studies (6/7) describing a positive association between these variants and severity of infection used bigdata analyses.
An analysis of 48,635 samples showed an average of 7. 23 mutations per sample  [76]. To date, 46,251 SARS-CoV-2 mutations were documented in the public databases (http://cov-glue. cvr. gla. ac. uk/#/replacement (accessed on 31 May 2021)). Numbers of variations are the highest in the NSP3 protein, followed by S protein, NSP12 protein, NSP2 protein, NSP 13 protein, NSP14, and NSP4 protein. By contrast, very little divergence was documented in NSP11, ORF10, ORF7b and E proteins  [74]  (http://cov-glue. cvr. gla. ac. uk/#/replacement (accessed on 31 May 2021)). Figure  2  shows the positions of the mutations and deletions in the genome and of amino acid substitutions in the virion. A key element of the coronavirus host range is determined by the binding affinity between the spike S protein and the cellular receptor.
It suggests that G614 may have a fitness advantage  [78]. The frequency of S protein 614G was significantly associated with high fatality rates in several countries, as reported in studies which analyzed SARS-CoV-2 sequences from GISAID database  [8, 10, 11]. However, clinical studies showed that this mutation did not correlate with severity of COVID-19, including mortality, transfer to ICU, mechanic ventilation, or length of stay at hospital  [30, 37, 41, 42, 44, 45]. the highest in the NSP3 protein, followed by S protein, NSP12 protein, NSP2 protein, NSP 13 protein, NSP14, and NSP4 protein. By contrast, very little divergence was documented in NSP11, ORF10, ORF7b and E proteins  [74]  (http://cov-glue. cvr. gla. ac. uk/#/replacement (accessed on 31 May 2021)). Figure  2  shows the positions of the mutations and deletions in the genome and of amino acid substitutions in the virion.