dataesr/scientific-paragraphs-categorization huggingface dataset:
Availability of Source Code and Requirements r Project name: Metagenomic read binning using sparse coding r Project home page: https://gitlab. com/kyrgyzov/lsa slurm r Operating system(s): Linux r Programming language: Python r Other requirements: NumPy, SciPy, Gensim, SPAMS (https:// gitlab. inria. fr/thoth/spams-devel) r License: MIT License r RRID:SCR 018134 r biotoolsID:Metagenomic read binning using sparse coding A lightweight implementation of key algorithms (including sparse NMF) is available from  [47].
1, 3, 4, 6 The thresholds that warrant further downstream testing have also been revised such that patients with a pre-test probability <5% are not recommended to undergo further testing, while testing is deemed appropriate in those with a pre-test probability >15%. Patients with an intermediate pre-test probability of 5-15% are recognized to have a good prognosis and testing may safely be deferred, although discretionary investigation can have a role. If testing is to be performed, the choice of test is determined by several factors, but in general, the guideline authors favour functional testing where the likelihood of obstructive disease is higher, and computed tomography coronary angiography (CTCA) where the likelihood is lower. In this study, we assessed the latest ESC-endorsed pre-test probability estimates for obstructive coronary artery disease in the Scottish COmputed Tomgraphy of the HEART (SCOT-HEART, NCT01149590) trial population.
Discussion We provide a global assessment of PA coverage on islands, utilizing an exhaustive database gathering information on 2323 islands. The average proportion of an island's area within a PA exceeds the 2020 country-based Aichi 11 conservation targets and is greater than the proportion of continental areas covered (22% compared to 15% for terrestrial areas and 13% compared to 7% for marine areas). However, there is considerable variability in PA coverage among islands globally. Nearly half the islands (>1000) have no terrestrial or marine PAs, whereas 335 islands have >90% of their terrestrial areas and 110 islands have >90% of their marine areas covered by PAs. We did not model this U-shape distribution to avoid focusing on extreme high values, but on certain thresholds that correspond to policy targets.
Sustainable fisheries can thus become an alternative to agriculture under climate change and land desertification. Unfortunately, spatial-based community management, tenured areas or OECMs are not currently represented in the World Database on Protected Area (WDPA) database, although some islands may prefer to adopt community-based management or multiple SDGs rather than pursuing western PA targets  5. The factors driving OECM establishment or coverage are still unknown and may differ from those highlighted in this study. Islands, if sufficiently protected, may play a key role in both biodiversity conservation and the achievement of multiple SDGs. Beyond extending the global coverage by PAs, the goal is to prioritize the right parts of Earth, that is, those concentrating small-ranged species  40.
We extracted the islands layer from the Global Administrative Areas-GADM v. 2. 8 (Global Administrative Areas 2016) spatial dataset, which maps administrative areas at a range of scales from national to local. GADM not only maps administrative areas at a high spatial resolution, but also for each area it provided attributes, such as the name and variant names. To aggregate the polygons into landmasses (islands and continents), we first dissolved all internal polygons, converted this output to the Behrmann (world) projected coordinate system for quantitative analyses, and then calculated the areas of all landmasses (km 2 ). All polygons >10 km 2 but excluding the continents (see above) were selected. From this output, all islands beyond 10 km from a continent were extracted using the "select by location" function in ArcGIS 10.
3. Finally, we intersected all polygons with the Gridded Population of the World, Version 4 (GPWv4) for the year 2015 and we only considered inhabited islands, resulting in a vector polygon dataset of 2503 islands. Terrestrial and marine coverage by PAs. To determine the percentage of protection for each individual island, we estimated the proportion of terrestrial and marine area, the latter defined as the part of the continental shelf in the territorial waters (12 nautical miles), which intersected with the WDPA on September 2017. Continental shelf has been extracted from the GEBCO-2014 grid at 30-arcsec resolution. From this grid, we selected all pixels between 0 and 200 m below sea level. From the WDPA, we considered all IUCN recognized PAs (categories I-VI). We obtained 118,609 terrestrial PAs, 2178 marine PAs, and 11,634 coastal (both terrestrial and marine) PAs.
For each island, we determined whether there was some protection on terrestrial and marine areas (coded as presence/absence so 1/0) and whether the protection coverage meets the conservation targets of the CDB set for 2020, so 17% for land and 10% for the sea, and the 2030 conservation targets, so 30% coverage on land and sea (coded as achieved/non-achieved so 1/0). Climate factors. We obtained the minimum and maximum temperatures from the WorldClim dataset (version 2), which provides a range of average monthly climate data for 1970-2000 at 30 s (~1 km²) resolution  74. Minimum and maximum temperatures of each island were calculated by overlaying each of the coolest and warmest months (raster) with the island layer (vector) using the "extract" function from the "raster" package in R-3. 4. For each island, we recorded the minimum and maximum rainfall from the driest and wettest month, respectively, in the same manner.
Geographic and habitat factors. Island area was calculated directly from the attribute table of island polygons (km 2 ) in ArcGIS 10. 3. We also calculated the distance of each island from the continent as the shortest linear distance between this island and the nearest continent in kilometers using the "Near" function in ArcGIS 10. 3. For altitude, each island polygon was intersected with the WorldClim dataset (1 km resolution), using the "Zonal Statistics" function in ArcGIS 10. 3 in the same manner as with temperature and rainfall, to extract the highest point, considered as the maximum altitude, and the mean island elevation. The fractal dimension of each island, as a proxy of tortuosity and habitat diversity, was calculated using the "classStat" function in R-3. 4 based on each island shape.
That grid cell at 30 arcsec (~1 km) consisted in the estimates of human population density based on counts consistent with national censuses and population registers, with cell values representing persons per square kilometer. For each island, we averaged the grid cell values intersecting with its polygon. Cultural factors. We estimated two variables for each island based on the diversity of languages: the total number of languages spoken in the country and the nature of institutional languages. For both variables we used the Ethnologue Global Dataset (20th Edition)  76. The total number of languages corresponds to the number of living languages in the sovereign state of the island, including both established and immigrant languages. The nature of institutional languages was coded in three categories: institutional languages are only indigenous (I), institutional languages are only non-indigenous (NI), institutional languages mix both indigenous and non-indigenous languages (M).
Administrative Areas , version 2. 8. Available at: www. gadm. org. Center for  International Earth Science Information Network (CIESIN) ,  Columbia University ,  2016. Gridded Population of the World, Version 4 (GPWv4):  Administrative Unit Center Points with Population Estimates. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). Available at: https://doi. org/10. 7927/H4F47M2C. Accessed 09/10/2017. IUCN and  UNEP-WCMC (2016 ), The World Database on Protected Areas (WDPA) [online], [ 09/2017 ]. Cambridge, UK:  UNEP-WCMC. Available at: www. protectedplanet. net. The GEBCO_2014 Grid, version 20150318. Available at: http://www. gebco. net. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology: https://www. worldclim.
All records and sightings will help to determine the extent of the INNT problem in a given area and facilitate a rapid response where necessary. They can also help to better understand species distribution, habitat suitability, and thus support better management. Such data should ideally be collected and quality-controlled by a (national / state) coordination centre, so that it can directly inform policy and management. Owing to the huge number of species observations that can be collected by non-professional scientists, citizen science has great potential to contribute to data collection, scientific knowledge on invasive non-native species, and to support early detection for NNTs outside cultivation areas. The recent adoption of information and communications technology in citizen science (e.
g. , web or mobile application-based interfaces for citizen training and data generation) has led to a massive surge in popularity, mainly due to reduced geographic barriers to citizen participation  (Adriaens 2015; Johnson et al. 2020). A rapid response capacity implies the availability of skilled personnel, contingency plans (where responsibilities are clearly determined), and technical guidelines for controlling invasive NNTs. Guidelines exist for many NNTs (e. g. , PM-9 for Ailanthus altissima, EPPO 2020) but they need to be incorporated into a unified framework and databases (Rec. 8). It should be stressed that controlling small foci of escaped NNTs, generally saplings, does not require heavy equipment and costly investments. In most cases control can be easily achieved either by cut-stump, drill-fill or hack and squirt techniques that do not require sophisticated tools.
2019) , habitats and regions at the global level. In 2020 the IUCN adopted as a formal standard the Environmental Impact Classification for Alien Taxa (EICAT) methodology  (Hawkins et al. 2015; IUCN 2020 ). Consideration should be given to assessing the impact of INNTs using EICAT. Results of such assessments should be shared using freely accessible platforms such as the IUCN Global Invasive Species Database. An important example of global network is the CONTAIN project, supported by a group of more than 20 researchers from four countries (Argentina, Brazil, Chile, and the UK) with diverse research focuses, such as invasion ecology of plants and animals, ecological restoration, economy, statistics, and social dimensions of invasions, which aims to design, and introduce to stakeholders a user-friendly decision making tool that will help to guide the long-term management of invasive species  (Lambin et al.
Information on NNTs and INNTs and strategies for dealing with them is critical for the implementation of all the recommendations in the GG-NNTs. Science-based strategies to tackle biological invasions depend on recent, accurate, well-documented, standardised, and openly accessible information on non-native species  (Hulme and Weser 2011; Groom et al. 2017). Information is becoming more easily accessible (e. g. , IUCN Global Invasive Species Database, www. iucngisd. org, IUCN Global Register of Introduced and Invasive Species, http://www. griis. org/, and CABI Invasive Species Compendium, www. cabi. org/ISC). For INNTs of concern in the European Union, IUCN provided comprehensive information on costs and available methods of appropriate management actions. Such science-based reviews are also available from the EPPO website; an example is the PM/9 Standard on Ailanthus altissima (EPPO 2020).
Acknowledgements We thank the  Czech Academy of Sciences, Institute of Botany, Department of Invasion Ecology ,  Czech Republic , for hosting a workshop on GG-NNTs in September 2019 and for creating the space that facilitated the production of this paper. We also gratefully thank  Rodrigo Pintos  for the  GG-NNTs logo ,  Ingolf Kühn , and  Benjamin Caldwell  for fruitful discussion and suggestions. AN, JP, PP and MV acknowledge funding from  EXPRO  grant no. 19-28807X  ( Czech Science Foundation ) and longterm research development project  RVO 67985939  ( Czech Academy of Sciences ). AP and BL were funded by Fondecyt  1180205 ,  CONICYT PIA AFB170008  and  NERC-CONICYT NE/S011641/1. DMR acknowledges the  DSI-NRF Centre of Excellence for Invasion Biology , the  National Research Foundation of South Africa , and the  Oppenheimer Memorial Trust  (grant  18576/03 ) for support.
GB acknowledges the  University of Sassari (UNISS)  for support through the  "fondo di Ateneo per la ricerca 2020 ". JLR acknowledges start-up funding from  Macquarie University's Faculty of Science and Engineering and Department of Biological Sciences. JRUW thanks the  South African Department of Forestry, Fisheries, and the Environment (DFFtE)  for funding, noting that this publication does not necessarily represent the views or opinions of DFFtE or its employees. MW was supported by  Research Programme   P4-0107  financed by the  Slovenian Research Agency. PEH was supported through grant  C09X1611  " Winning against Wildings " from the  New Zealand Ministry of Business, Innovation and Employment. US was supported by the  Swiss Programme for Research on Global Issues for Development (r4d)  for the project " Woody invasive alien species in East Africa: Assessing and mitigating their negative impact on ecosystem services and rural livelihood " (Grant Number:  400440_152085 ) and by  CABI  with core financial support from its member countries (see www.
A provides scaling relations of the transport parameters with L for stable secondary speciesthey extend, for all transport parameters, those that were given in App. C of  Génolini et al. (2019) ; App. B highlights the constraints on L set by various 10 Be/ 9 Be datasets (low-energy experiments, ISOMAX, and preliminary PAMELA analysis); App. C outlines why 7 Be/( 9 Be+ 10 Be), the most favourable isotopic ratio to extract experimentally, is of no practical use to constrain L.
CR datasets and modulation levels This analysis is based on several datasets. Each dataset is associated to a solar modulation level depending on its data taking period. We use here the simple force-field approximation  (Gleeson & Axford 1967 , 1968 , and Caballero-Lopez & Moraal 2004) , whose single parameter, φ FF , is taken as a nuisance parameter in the analyses. As detailed in  Weinrich et al. (2020) , the solar modulation level (prior) for each dataset is based on the analysis of neutron monitor data  (Maurin et al. 2015; Ghelfi et al. 2016 Ghelfi et al. , 2017a,b),b). As in  Weinrich et al. (2020) , the baseline data used to fix the transport parameters are AMS-02 Li/C and B/C data  (Aguilar et al. 2018a )-denoted 'base' in the following. The halo size L is then constrained by combining the base with several ratios from different datasets.
The largest dataset is that of AMS-02 Be/B, covering ∼ 3 GV to ∼ 2 TV. We also have several low-energy 10 Be/ 9 Be or 10 Be/Be datasets available, retrieved from CRDB 2  (Maurin et al. 2014). Except for the ISOMAX data reaching Table  2. List of experiments with their data-taking periods and associated expected Solar modulation level. In the list below, datasets from the same experiment and data taking periods share a common Solar modulation level in the analyses. PAMELA data are from a preliminary analysis. For this reason, they are never considered in our main results, and are only used for illustration in App. B. Experiment (period) φ prior ∼ 2 GeV/n, most of them (ACE, IMP7&8, ISEE3, Ulysses, and Voyager1&2) are at a few hundreds of MeV/n. The various datasets of interest are listed in Table  2 , along with their estimated Solar modulation level and bibliographic reference.
Several ratio and dataset combinations are considered, in order to assess and compare their respective impact on L. Combined to 'base' (i. e. Li/C and B/C data only), which enables the determination of the transport parameters, at least one 10 Berelated dataset is necessary to determine L. In particular, for fits involving 10 Be/ 9 Be, we differentiate three groups (see Table  2 ): the most precise low-energy data only ('ACE'), low-energy data without ACE ('LE w/o ACE'), or intermediate energy data only ('ISOMAX'). For consistency for the Solar modulation nuisance parameters, in all fits involving low-energy isotopic ratios, we consider, if available, the associated similarly modulated Li/C and B/C low-energy data (in addition to AMS-02 data). 3. 2. Expected constraints from B/C, Be/B, and 10 Be/Be Before moving to the fits and results, we wish to understand how strongly L can be constrained by different data combinations of 10 Be.
This gives a slightly lower best-fit value and uncertainties compared to 10 Be/Be data, but this apparent improvement is related to a tension between ISOMAX data and all the others. The reduced uncertainties result from an attempt to accommodate all the data Table  3. Halo size fit results for the combined analysis of Li/C and B/C (denoted 'Base', see also Fig. 2 ) with an 'unstable-to-stable' secondary ratio r. The top rows show the constraint from AMS-02 data (r = Be/B), while the bottom rows show the combined constraint from all available datasets (r = Be/B + 10 Be/Be + 10 Be/ 9 Be). We gather in Table  3  the best-fit values and 1σ uncertainties on L for the AMS-only analysis (with Be/B, top) and the combined analysis (with Be/B and all isotopic ratios, bottom). In terms of the χ 2 min /dof values, a fair but not perfect agreement is obtained when using AMS-02 only data (χ 2 min /dof ∼ 1.
BIG χ 2 nui /n nui ≡        n s s=0 N s Sol. Mod. + n x x=0 N x XS        /(n s + n x ), (3) with N s Sol. Mod. and N x XS the n s and n x nuisance parameters for solar modulation and cross sections respectively (n nui = n s + n x ). As discussed in  Weinrich et al. (2020) , this quantity gives a direct check that nuisance parameters behave properly. On average, nuisance parameters post-fit values should never be more than 1σ away from their prior, that is, χ 2 nui /n nui 1, and this is verified for all our fits. For illustration purposes, we finally show in Figs. 3  and 4  the model calculation and the data for Be/B and isotopic ratios. The parameters are taken from the best-fit to all combined Be/B, 10 Be/Be, and 10 Be/ 9 Be data (last column in Fig. 2 ). In both plots, the top panels show the model calculations for the three transport configurations (BIG, SLIM, and QUAINT) along with the data.
lowed for δ 0. 3, growing to larger than 10 kpc for δ ≈ 0. 5. This tension with 10 Be/ 9 Be data was attributed to cross-section uncertainties-their importance in limiting the prediction power of Be/B is detailed in  Tomassetti (2015b). Using updated cross sections and propagating their uncertainties, we find here L = 5 +3 -2 kpc based on AMS-02 Be/B data, which is now compatible with values derived from ACE-CRIS 10 Be/ 9 Be data. Similar values were found by the GALPROP team, using both ACE-CRIS and AMS-02 Be fluxes (without 10 Be/ 9 Be), with L = 4 ± 0. 6  (Boschini et al. 2020). This stronger constraint is somehow in line with the fact that combining ACE-CRIS and AMS-02 data lead to tighter constraints (see Table  3 ), but as these authors do not use 10 Be/ 9 Be, it is most probably attributed to a larger extent to the fact that they do not propagate cross-section uncertainties.
Constraints on L To check the compatibility with the limits set by AMS-02 Be/B data, we carried out calculations for several L values. The latter are taken inside their allowed range (upper-half of Table  3 ). To ensure a consistent calculation, the transport parameters are rescaled with L according to Eq. (A. 1) and values in Table  A. 1 (see App. A). We set conservative limits on L by excluding propagation setups for which the prediction of the secondary positron flux exceeds the AMS-02 measurements  (Aguilar et al. 2019a ) by more than 3σ in a single bin. In practice, for all our configurations, the lowest-energy AMS-02 data point is the one setting constraints. In a first step, we check the consistency of the positron flux with the Be/B constraint. In Fig. 5 , we report our predictions for the secondary positron fluxes (rescaled by E 3 ) for the BIG, SLIM, and QUAINT propagation models (left, middle, and right panel, respectively) along with the AMS-02 data  (Aguilar et al.
2019a ). These predictions are derived using the best-fit parameters inferred from the combined Li/C, Be/C, and B/C analysis performed in  Weinrich et al. (2020) , already extensively discussed throughout the paper. For illustration, we have taken the best-fit values obtained for L from the AMS-02 Be/B data only (see Table  3 ). In each panel, the solid curve corresponds to the best-fit value for L, bracketed by its 1σ statistical uncertainty (dashed curve for the lower deviation, and dot-dashed curve for the upper deviation). From Fig. 5 , we see that the positron constraint on L is only relevant for the QUAINT propagation model. The latter has an effective Alfvèn speed V A ∼ 40 km/s (see Table  A. 1) which sets the amplitude of reacceleration 7. For the BIG and SLIM models (left and middle panels), both devoid of reacceleration, the nominal predictions undershoot the data.
However, primary positrons, already necessary to explain the high-energy positron fraction in pre-AMS-02 studies, might make up a significant fraction of the positron budget also at low energy-for example, the absence of primary positrons at low energy was raised as an important issue for astrophysical sources like pulsar wind nebulae in  Blasi & Amato (2011). Therefore, discussing further the consistency of any propagation model (not only BIG and SLIM) with the positron data should rely on analyses also including primary positrons. This goes beyond the scope of this paper.
Conclusions In the context of recent high-precision AMS-02 data, we have revisited the constraints set on the halo size of the Galaxy from radioactive species and positron fluxes. Using AMS-02 Be/B data we find L = 5 +3 -2 kpc at 1σ, in agreement with  Boschini et al. (2020)  (but with a larger error) and less than one sigma away from the results of Evoli et al. 3 )-AMS-02 data are from  Aguilar et al. (2019a). Fig. 6. Secondary positron flux predictions using the best-fit transport parameters of the QUAINT propagation model (also inferred from the AMS-02 combined Li/C, Be/B, and B/C analysis). We show the predictions associated with the 3σ limits on L assuming different solar modulation strengths. (2020). Our result holds for several transport configurations  (Derome et al. 2019; Weinrich et al.
2020) , namely BIG (reacceleration and low-rigidity break), SLIM (pure diffusion with lowrigidity break), and QUAINT (reacceleration and diffusion upturn in the non-relativistic regime). The constraints are tighter (factor ∼ 2 reduction in errors) and move to a lower value of L (by 0. 3-0. 7 kpc) when low-energy 10 Be/ 9 Be data are considered, but this tightening may be related to the fact that ISOMAX data  (Hams et al. 2004 ) prefer a smaller halo size L ≈ 3 kpc and might be indicative of a tension in the data. With the recent release of 3 He and 4 He data  (Aguilar et al. 2019b ), AMS-02 demonstrated its capabilities for measuring isotopic fluxes. Separating Be iso-topes will certainly be even more challenging, but AMS-02 will provide a unique picture in the few GeV/n regime, where only ISOMAX and preliminary PAMELA data  (Bogomolov & Vasilyev 2019)  are available for now, and shed some light on the mutual consistency of these datasets.