dataesr/scientific-paragraphs-categorization huggingface dataset:
High levels of threat to the tropical African flora By applying our novel PACA approach to a large, taxonomically and geographically verified database of vascular plant occurrences in tropical Africa  (12) , we conducted the first ever evaluation of the potential conservation status of an entire flora on a continental scale using key criteria used for IUCN Red List assessments. Our results suggest that around one-third (31. 7%) of tropical African vascular plant species are potentially or likely threatened by extinction (17. 3% as LT and 14. 4% as PT, Table  1 ). Our study thus provides further evidence that the flora of tropical Africa will be highly vulnerable in the future  (17) (18) (19). This situation will no doubt be magnified by the effects of climate change, which is one of the most important assumptions influencing extinction risk  (20).
Unfortunately, identifying the proportion of threatened species in a given area using the IUCN Red List is complicated by the fact that only a small proportion of plant species have been assessed to date (as illustrated by the fact that only 9. 1% of the species in our dataset have full post-2001 assessments). Moreover, Red List efforts have often focused primarily or exclusively on narrow-range species, yielding biased results  (24)  that preclude an accurate estimate of the proportion of threatened species, a situation that justifies the rapid and preliminary conservation assessment batch procedure developed here. In our study, we undertook preliminary assessments for 22,036 vascular plant species, representing around 68% of the tropical African diversity  (25). Automated conservation assessment approaches like the one used here were recently shown to perform well in correctly assessing threat categories when compared to full assessments  (26).
Definition of PACA The rationale behind PACA is fourfold. First, the PACA approach in part follows the IUCN Red List, which is the most recognized and widely used source of information on conservation status (3). While other methods exist, using anything other than the IUCN Red List runs the risk of having limited impact as the resulting assessments are unlikely to be taken into account for conservation planning and policy-making. Second, assessing conservation status for large numbers of taxa using the huge quantities of data available via open access portals [e. g. , Global Biodiversity Information Facility (GBIF): www. gbif. org and The Living Atlas of Australia: www. ala. org. au] or datamobilization projects [e. g. ,  (12) ] by means of a full IUCN Red Listing process would be prohibitively time consuming and expensive.
These species are not currently thought to be threatened but will very likely become so in the near future, and because of their limited distribution, could also quickly become extinct. Application of the Red List Category VU D2 requires that there be a plausible future threat that could rapidly lead to CR status, such as the planned construction of a dam in the case of a riparian species or hotel development for a coastal species. According to the IUCN Red List guidelines, a species can be categorized as Data Deficient (DD) when "there is inadequate information to make a direct, or indirect, assessment of its risk of extinction based on its distribution and/or population status. " Data deficiency in conservation assessments can be the result of (i) taxonomic uncertainty and/or (ii) insufficient information.
This comparison should be interpreted with caution because full assessments and PACA were not conducted on the same dataset. Our result is, however, in line with the findings of Nic Lughadha et al. (26) , who showed that applying default settings of the ConR R package  (38)  (hence only applying Criterion B without considering habitat decline, as we have done here) on several plant datasets provided very good sensitivity. As noted by these authors, the conservative nature of ConR (and thus by extension the PACA approach) is desirable to minimize the risk of missing threatened species. With the recent advent of high volume herbarium digitization efforts, large-scale databases of plant species distributions are now widely available. These databases must, however, be accurately checked and taxonomically verified, as has been done for the database used here, RAINBIO  (12).
Several other databases have also been published for tropical areas  (39). Those that have not been taxonomically verified should be used with caution. The GBIF, the most widely consulted repository of biodiversity distribution data (www. gbif. org), presents an invaluable source for species distribution data. Using GBIF applications such as PACA, however, requires careful screening because it comprises an unverified compilation of information drawn from multiple datasets and thus contains inaccurate identifications as well as duplicate records  (8)  that may be assigned to different species because of a lack of taxonomic standardization. The quality of the data served by GBIF thus depends solely on the original provider. If used as the first source for PACA analyses, data from GBIF thus require significant initial verification to assess their general quality, remove duplicate records, correct inaccurate identifications, check for taxonomic synonyms, and correct erroneous collection coordinates  (40).
Conclusions The comparatively small number of species whose risk of extinction has been fully assessed for the IUCN Red List partly reflects difficulties in accessing reliable specimen information. However, because many datasets have recently become available for such analyses, the primary limiting factor today is the time-consuming nature of conducting full assessments coupled with a lack of well-trained assessors. Using the PACA approach proposed here, we were able to assess more than 22,000 species rapidly (6 to 7 hours) and in a way that is aligned with the IUCN guidelines. On the basis of our experience in conducting full Red List assessments in Madagascar and Continental Africa  (36) , we estimate that the process of assessing a single species, from data compilation and verification to publication on the Red List, costs between $30 and $500 and takes on average one person/day of effort.
The actual cost depends, however, on the number of specimens and the quality and quantity of the associated information (i. e. , assessing a well-documented timber species could take several days and cost 20 times more than a poorly collected species), in addition to the effort required to review and validate the assessment. Thus, in situations where a verified dataset is available, PACA provides a cost-effective way to initiate the Red List assessment process for a large number of species. This is even more relevant as the process of generating taxonomically verified and high-quality species distribution becomes increasing automated (see, for example, BIEN, http://bien. nceas. ucsb. edu/bien/biendata/bien-3/). We thus predict that approaches such as PACA will become particularly useful in the near future.
Flora of tropical Africa dataset We used a taxonomically verified database of tropical African vascular plant species distribution: RAINBIO  (8, 12). This database contains 590,231 georeferenced records representing distribution information for 25,222 native species in sub-Saharan Africa, excluding Madagascar and southern Africa. Invasive or planted or cultivated species were not included [see  (12)  for details on cleaning and quality-checking the data]. Global assessments require knowledge of all known occurrences for a given taxon. Non-endemics to our study area, i. e. , species with occurrences both within and outside tropical Africa, are problematical because RAINBIO does not include occurrences records from outside the continent  (12). A species with a few records in tropical Africa might be assessed using the PACA approach as potentially threatened, although it is widely distributed outside Africa and therefore likely not threatened.
To reduce this bias, we first excluded occurrences of species only found in South Africa, Swaziland, and Lesotho because only a small portion of the available records for these countries were included in the RAINBIO database (this concerns 2254 species and 3332 occurrences). We then applied the following procedure to identify species whose range extends beyond our study area by comparing the distribution as described by the RAINBIO database to that based on records from GBIF. Specifically, we searched GBIF for occurrences of 22,968 species in the RAINBIO database using the "rgbif" R package  (44)  and extracted occurrences for each from GBIF (excluding those with georeferencing issues, R code available at https://github. com/gdauby/stevart_el_al_PACA). On the basis of these GBIF occurrence records, the number of occupied cells at 10-km resolution was calculated, as well as the number of continents in which the species has been recorded.
Species identified by PACA as potentially threatened under Criterion B based on the RAINBIO dataset (see the method explaining the preliminary assessment below) and occupying more than 10 10 km × 10 km cells based on the GBIF data were tagged. GBIF occurrences were found for 21,345 species (96. 9% of those in the RAINBIO dataset). Using RAINBIO, the number of species classified as potentially threatened under Criterion B (i. e. , belonging to CR, EN, or VU categories, see below) was 15,470. Among these species, 1220 were recorded from more than 10 10 km × 10 km cells in the GBIF dataset, indicating that subpopulations for these species are missing in the RAINBIO database. When scrutinizing these species, it was found that the additional subpopulations are often artifacts of georeferencing errors or involved doubtful records.
Hence, because the rationale of PACA is to provide preliminary conservation assessments, we adopted a conservative approach and only removed species occurring in more than two continents  (20 species)  or occupying more than 15 10 km × 10 km cells based on the GBIF dataset (912 species) to reduce the risk of incorrectly removing species that are truly threatened. The final dataset used in this paper thus comprised 580,208 distribution records for 22,036 species. Hence, from the initial list of 25,222 species, a total of 3186 were judged to have their known occurrences poorly covered by the RAINBIO database and/or have distributions likely to lie primarily outside our tropical African study area and were thus not assessed here.
g. , georeferenced herbarium specimens) for assessments using Criterion B  (26). As part of the PACA process, we generated a framework for estimating whether a species faces potential population reduction (required for Criterion A) or future decline in key geographic parameters (needed for Criterion B). Thus, PACA is aligned with two key elements of the IUCN Red List: Criterion A, relating to population size reduction, and Criterion B, relating to geographic range. Approaches aligned with the three other Red List criteria were not implemented as they require data that are unavailable for most plant species, especially across the tropics. All IUCN parameters needed for preliminary assessments of taxa based under Criterion B (see below) were calculated using the R package ConR ver. 1. 2. 1  (38).
First, all the occurrences found within a single protected area were considered to represent a single location, based on the rationale that these occurrences would be equally affected by a single event such as downgrading, downsizing, or degazetting the protected area [e. g. ,  (37) ], although we acknowledge that these events can have various impacts that are hard to estimate at broad scales. The number of locations was thus estimated using a shapefile of terrestrial protected areas for tropical Africa downloaded from the World Database on Protected Areas (www. protectedplanet. net). Second, for occurrences located outside protected areas, the number of locations was estimated as the number of occupied cells within a superimposed grid of 10 km 2. This grid cell size is suggested as a suitable proxy for detecting a threat that would equally affect all individuals of a taxon contained therein [e.
Geographic distribution of threatened species After species were assigned to a PACA category (Table  1 ), we compiled distributional data to summarize and map the estimated level of threat across Africa. A gridded spatial representation of threat was undertaken using an "adaptive resolution" SU method  (49). This approach adapts the size of the SU as a function of a userdefined threshold of minimum occurrence records. A shapefile of the adaptive SU grid was created by uploading the RAINBIO database to the Infomap Bioregions application (49) using the following parameters: maximum cell capacity = 1000, minimum cell capacity = 250, maximum cell size = 8°, and minimum cell size = 0. 5°. We also represented our results for each country and each terrestrial ecoregion  (33)  found in Africa. In each case (grids, adaptive SU, and ecoregion), we estimated the total number of taxa recorded and the proportion of taxa assessed as LT and PT, under Criteria A and B separately, and by combining both criteria, i.
Acknowledgments Funding: This work was cofunded by the  French Foundation for Research on Biodiversity  and the  Provence-Alpes-Côte d'Azur région through the Centre for Synthesis and Analysis of Biodiversity Data Program , as part of the RAINBIO research project through funding to T. L. P. C. (http://rainbio. cesab. org/). G. D. was partly funded by the  Belgian Fund for Scientific Research (F. R. S. -FNRS ). J. -C. S. and A. B. -O. acknowledge economic support from  VILLUM FONDEN  through the  VILLUM Investigator project  " Biodiversity Dynamics in a Changing World " (grant  16549 ). Author contributions: T. S. , G. D. , and T. L. P. C. conceived the study; T. S. , G. D. , P. P. L. ,.
Search for long-lived particles produced in p p collisions at √ s = 13 TeV that decay into displaced hadronic jets in the ATLAS muon spectrometer The ATLAS Collaboration A search for the decay of neutral, weakly interacting, long-lived particles using data collected by the ATLAS detector at the LHC is presented. The analysis in this paper uses 36. 1 fb -1 of proton-proton collision data at √ s = 13 TeV recorded in 2015-2016. The search employs techniques for reconstructing vertices of long-lived particles decaying into jets in the muon spectrometer exploiting a two vertex strategy and a novel technique that requires only one vertex in association with additional activity in the detector that improves the sensitivity for longer lifetimes. The observed numbers of events are consistent with the expected background and limits for several benchmark signals are determined.
The paper first describes the ATLAS detector in Section 2, followed by the event selection strategy in Section 3, the benchmark models in Section 4, and the data and simulation samples in Section 5. The specialized trigger and reconstruction algorithms are discussed in Section 6, followed by a description of the baseline selection applied to all events in Section 7. The next two sections (8 and 9) outline the three search topologies. Systematic uncertainties are summarized in Section 10 and results for all three topologies are presented in Section 11. The summary and conclusions are given in Section 12.
Because the tubes are 2-5 m in length with a direction along φ, the MDT measurement provides only a very coarse φ position of the track hit. In order to reconstruct the φ position and direction, the MDT measurements are combined with the φ coordinate measurements from the trigger chambers. The ATLAS trigger and data acquisition system  [36]  consists of a hardware-based first-level trigger (L1) followed by a software-based high-level trigger (HLT) that reduces the rate of recorded events for offline storage to 1 kHz. The implementation of the L1 muon trigger logic is similar for both the RPC and TGC systems. Each of the three planes of the RPC system and the two outermost planes of the TGC system consist of a doublet of independent detector layers. The first TGC plane contains three detector layers.
Data and simulation samples The analysis presented in this paper uses √ s = 13 TeV pp collision data recorded by the ATLAS detector with stable LHC beams during the 2015 and 2016 data-taking periods. After data quality requirements, the total integrated luminosity is 3. 2 fb -1 and 32. 9 fb -1 for 2015 and 2016, respectively. Zero-bias data are used to estimate the expected background for the 2MSVx strategy and potential contamination by non-collision background in the 1MSVx+AO strategies. These data are acquired with a special trigger which fires on the bunch crossing that occurs one LHC revolution after a low-threshold calorimeter-based trigger and therefore have a negligible signal contamination. The zero-bias trigger runs throughout ATLAS data taking, so these data are acquired with the same beam conditions present in normal physics data and can be used to study the expected background.
Due to the very high output event rate, the zero-bias trigger is prescaled and only a fraction of the total events are recorded. For this reason, the integrated luminosity acquired is much lower than the total collected during 2015 and 2016 data taking and corresponds to 1. 1 µb -1 and 12 µb -1 for the two periods, respectively. Monte Carlo (MC) simulation samples were produced for all models considered in this paper. The masses, summarized in Table  2 , were chosen to span the accessible parameter space. For the Stealth SUSY model, the singlino and singlet masses were set to 100 GeV and 90 GeV, respectively. These values were recommended by the authors of the model as a good representative choice  [7]. The small mass-splitting between the singlino and singlet ensures that the gravitino carries off very little momentum.
Background estimation To estimate the expected background for the 2MSVx strategy it is necessary to quantify the frequency with which the MS vertex algorithm reconstructs isolated vertices for non-signal events. This number can be calculated from data using events with one isolated MS vertex which pass either the Muon RoI Cluster trigger or a zero-bias trigger. The expected background with two isolated MS vertices is calculated as follows: N 2Vx = N 1cl • P Vx noMStrig + N 2cl 1UMBcl • P Vx Bcl + N 2cl 1UMEcl • P Vx Ecl. The events selected by the Muon RoI Cluster trigger and containing only one MS vertex are separated into those containing only one cluster of muon RoIs (N 1cl ), and those containing two muon RoI clusters, where only one cluster is matched to the reconstructed MS vertex and the other is unmatched in the barrel or endcaps (N 2cl 1UMBcl ,N 2cl 1UMEcl ).
The term P Vx noMStrig is the probability of finding a vertex in events not selected by the Muon RoI Cluster trigger. This probability is determined from zero-bias events by dividing the number of good, isolated MS vertices not passing the muon RoI Cluster trigger by the total number of zero-bias events that satisfy standard event quality criteria. The terms P Vx Bcl and P Vx Ecl are the probabilities for finding an MS vertex given a muon RoI cluster in the barrel and endcaps, respectively. Since zero events are observed with two trigger clusters and one vertex, the contribution of the N 2cl terms is negligible. Therefore, the number of two-MS-vertex events can be calculated as N 2Vx = N 1cl • P Vx noMStrig. Six good isolated MS vertices were found in 35,673,956 zero-bias triggered events, while 159,816 events with one isolated muon RoI cluster matched to a vertex were selected in the 2015 and 2016 datasets.
1MSVx+Jets strategy The main criterion that is used to distinguish a signal MS vertex from background is its degree of isolation as described in Section 8. 1. To characterize the degree of isolation with a single value, the variable ∆R min = min ∆R(vertex, closest jet), ∆R(vertex, closest track) was defined. Figures  4(a ) and 4  (b)  show the distributions of the isolation variable used for 1MSVx+Jets events for data and some of the MC benchmark samples for barrel and endcap vertices, respectively. Another signal selection variable is the sum of the number of MDT hits and trigger hits (RPC and TGC in the barrel and endcaps, respectively) in a cone around an MS vertex, since a signal event is expected to leave more hits than a background one. Figures  5(a ) and 5  (b)  present the distributions of the number of MS hits variable used for the 1MSVx+Jets strategy for data collected during 2015 and 2016 and for some of the MC benchmark samples for barrel and endcap vertices, respectively.
The selection requirements that define the two regions were optimized in order to ensure a sufficient signal acceptance and background rejection. For events with a barrel MS vertex the number of hits (n MDT +n RPC ) is required to be greater than 1200, while for events with an endcap MS vertex, the number of hits (n MDT +n TGC ) must exceed 1500. n MDT + n RPC > 2000 n MDT + n TGC > 2500 ∆R min > 0. 3 ∆R min > 0. The signal region selection for the 1MSVx+E miss T strategy was optimized using signal acceptance versus background rejection estimated from data in the background-dominated region. The selection requires values of at least 0. 8 for isolation ∆R min and 1. 2 for |∆φ(E miss T ,MSVx)|, for both the barrel and endcaps. Due to the higher levels of background, the selection requirement imposed on the isolation is stricter than the ones used for the 1MSVx+Jets strategy.
Background estimation The ABCD method developed for the 1MSVx+AO strategies uses two, uncorrelated vertex-based variables to create a two-dimensional plane that is split into four parts: region A is where most signal events are located, and three control regions (B, C, and D) that contain mostly background. The number of background events in A can be predicted from the population of the other three regions:  The ABCD method relies on there being only one source of background, or multiple sources that have identical distributions in the ABCD plane. In general, non-collision background, which does not originate from the pp interaction point, will have a different distribution in the ABCD plane. To determine the non-collision background contamination, data collected in LHC empty bunch crossings throughout the 2016 data-taking period were used to estimate the number of non-collision background vertices in coincidence with events otherwise satisfying the single-vertex selection criteria.
The overall systematic uncertainty of the two-jet efficiency was determined by adding each component in quadrature and varies from 0. 3% to 9. 8%, depending on the sample and detector region. Systematic uncertainties due to the E miss T computation for the 1MSVx+E miss T strategy are negligible. By comparing the average number of muon segments in a cone around punch-through jets in data and MC simulation, systematic uncertainties associated with the mismodeling of the MS vertex reconstruction in signal events were found to be negligible. For each of the scalar boson samples, excluding m s = 100 GeV, two proper lifetime points were fully simulated: one nominal sample and a secondary sample with longer proper lifetime, as described in Section 5. The secondary sample is used to validate the extrapolation procedure, and a systematic uncertainty is assigned to each sample due to the non-closure of the extrapolation procedure.