dataesr/scientific-paragraphs-categorization huggingface dataset:
These topologies are also relevant to validate the implementation of the b-tagging efficiencies provided as auxiliary material. Finally, the CMS collaboration has provided covariance matrices to allow for the statistical combination of the relevant signal regions using the simplified likelihood framework  [85]. All the limits shown in this work were computed using these covariance matrices, following the SModelS  [86]  implementation. Further details of the implementation can be found in https://github. com/andlessa/RDM/tree/master/myCheckMate3Files/ validation. We display in figure  19  the validation figures obtained in the compressed stop scenario (left panel), and for the sbottom case (right panel). The solid red curves correspond to the exclusion obtained with our CheckMATE 2 implementation, while the solid black curve corresponds to the official CMS curve.
C. 2 ATLAS bb+MET In addition to the CMS search for b-jets plus MET, we have also considered the corresponding ATLAS search (ATLAS-SUSY-2018-34  [46] ) that relies on a luminosity of 139 fb -1 of data. For this search, however, we do not rely on a detailed recasting of the event selection, but simply apply the upper limits obtained for the sbottom simplified model (pp → bb , b → b χ0 1 ) to the channel pp → χ 1 χ 1 , χ 1 → ν bχ 0 , as discussed before. Since no auxiliary material was available until the completion of this work, upper limits on sbottom pairproduction cross sections were digitised and included in a private version of the SMod-elS  [56]  database. SModelS was then used to compute the effective signal cross section, σ(pp → χ 1 χ 1 ) × BR(χ 1 → bνχ 0 ) 2 , which was next compared against the official upper limits implemented in the database.
Acknowledgements We would like to thank  David Marzocca  and  Marco Nardecchia  for their collaboration in the early stages of this work, and we would like to express our gratitude to the organisers of the  Les Houches "Physics at TeV Colliders" 2019 Session  for setting up an interesting workshop and providing an ideal atmosphere for scientific exchange. BF and AJ would like to thank  Abdollah Mohammadi  for kindly providing cutflow tables for some benchmark scenarios and for his assistance throughout the implementaion and the validation of the CMS leptoquark plus missing energy analysis in MadAnalysis 5. The work of AJ is supported in part by a  KIAS Individual  Grant No. QP084401  via the  Quantum Universe Center at Korea Institute for Advanced Study  and by the  National Research Foundation of Korea , Grant No.
NRF-2019R1A2C1009419. The work of AL was supported by the  São Paulo Research Foundation (FAPESP) , project  2015/20570-1. JH acknowledges support from the  DFG  via the Collaborative Research Center TRR 257 and the  F. R. S. -FNRS  as a Chargé de recherche. The work of AP and GB was funded by the  RFBR  and  CNRS  project number  20-52-15005. The work of AP was also supported in part by an  AAP-USMB  grant and by the  Interdisciplinary Scientific and Educational School of Moscow University for Fundamental and Applied Space Research. The work of DS is based upon work supported by the  National Science Foundation  under Grant No. PHY-1915147. JZ is supported by the  Generalitat Valenciana (Spain)  through the  plan GenT program  ( CIDEGENT/2019/068 ), by the  Spanish Government  ( Agencia Estatal de Investigación ) and ERDF funds from  European Commission  (Grant No.
Study setting: the Coverage trial Coverage was a multi-site phase IIb-III outpatient randomized controlled clinical trial which aimed to evaluate the efficacy and safety of drug therapies to reduce the risk of worsening in at-risk individuals aged 50 years and above with early symptomatic COVID-19 infection (less than 7 days) (NCT04356495)  [21]. The Coverage trial protocol was submitted to a French Ethics Committee (CPPIDF1-2020-ND45) and to the French National Agency for the Medicines Safety (ANSM) on 31 March 2020. The trial activities started on 16 April 2020 and the first patient enrolment was on 29 July (see Supplementary material 2). First implemented in Bordeaux, the trial had 14 trial centres in 9 French regions in June 2021, each centre having its own operational organization. The trial ended on 3 December 2021.
Study design, study population and data collection We conducted a cross-sectional mixed methods substudy among stakeholders involved between March and June 2020 in the Coverage trial design, coordination and implementation process. An exhaustive list of stakeholders was drawn by the trial manager and principal investigator (PI). In July-August 2020, all stakeholders were invited by email to respond to an online self-administered 15-min questionnaire (See Supplementary material 3) covering the following domains 1) Sociodemographic characteristics, 2) Experience in clinical research before the Coverage trial, 3) Motivations to take part in the Coverage trial, 4) Experience within the Coverage trial, 5) Perceptions and lessons learned from the Coverage trial experience and more globally regarding outpatient therapeutic trials in emergency epidemic contexts.
In addition, previously published sequences for both ridley species were downloaded from GenBank (Table  S1 ). Some of the earliest published data were limited to short segments (~ 470 bp). Here we only included longer sequences (> 700 bp) to potentially increase power to resolve population structure. In total, 283 Kemp's ridley  (Duchene et al. 2012; Frey et al. 2014; Reid et al. 2019; Frandsen et al. 2020 ) and 340 olive ridley sequences were obtained from published studies  (Duchene et al. 2012; Plot et al. 2012; Jensen et al. 2013; Revuelta et al. 2015; González-Paredes et al. 2017; Campista León et al. 2019; Work et al. 2019; Adnyana et al. 2020; Silver-Gorges et al. 2020; Stelfox et al. 2020). Several segments from Madagascar and India  (Stelfox et al. 2020)  were excluded because haplotype frequencies were not available, and only publicly accessible haplotypes from  Adnyana et al.
(2020)  were included. Sequences were aligned and truncated to the most common overlapping region, a 653 bp segment used to analyze all 1,230 individuals. For consistency across studies, the olive ridley sequences were classified following NOAA's nomenclature. In the absence of a similar system for Kemp's ridleys, the   S1  and S2. Putative geographical barriers are shown by dotted bars STRUCTURE 2. 3. 4 uses an MCMC procedure to estimate the posterior probability in which the data fit the hypothesis of K clusters [Pr (X|K)]. Because of differences in sample sizes between populations, the recommended priors in  Wang (2017)  were employed. An uncorrelated allele ancestry prior and alpha as 0. 1 were used to account for the ten sampling sites. First, the number of clusters was tested by performing 20 replicates for each K from 1 to 15, with 1,000,000 MCMC iterations, 10% burn-in, and no prior information on sampling location.
The dapc() function from the R package adegenet  (Jombart and Ahmed 2011)  was used, and the optimal number of clusters was predicted using the sequential K-means method and then the Bayesian Information Criterion (BIC) to choose the best K from 1 to 20. The number of principal components that explained 90% of the cumulative variance was retained. DAPC was also run with sampling areas as prior information. Bayesian inference was employed to estimate demographic parameters and test 21 colonization models between the three ocean regions (Fig. S1 ) using Migrate-n v. 4. 4. 0  (Beerli and Felsenstein 2001). Demographic parameters estimated (using an island model) were theta (as 4Neµ, where Ne is the effective population size and µ is the neutral mutation rate per site per generation) for each ocean region, and the asymmetrical immigration rate (M = m/µ) between each one.
Microsatellites Nuclear DNA variation was assayed for 285 global L. olivacea samples using fifteen microsatellite loci (Table  S1 ). For details on genotyping, see Supplementary Text 2, and for a description of primers used, see Table  S3. The microsatellite data were screened for null alleles and genotyping errors (e. g. , stuttering) using Micro-Checker  (Van Oosterhout et al. 2004) , and linkage disequilibrium between loci was estimated using Arlequin. Although  Silver-Gorges et al. (2020)  found three of these loci linked in populations from Costa Rica and Mexico, no consistent linkage pattern within populations was detected when analyzing the present worldwide dataset and thus all 15 loci were retained. The following diversity indices were calculated with Arlequin: the number of alleles per locus (K), observed (Ho), and expected heterozygosity (He) under Hardy-Weinberg equilibrium, with sequential Bonferroni corrections (p = 0.
One or two substitutions separated the regional clades, while the most frequent haplotypes within each ocean region presented a star-like pattern with a central sequence from which others branched off. The most common ATL haplotype (Lo67) was separated from the IWP clade by one substitution (Fig. 2 ). Geographic structure between ocean regions was indicated as only one breeding female with an mtDNA haplotype from another region was found: a female nesting in eastern Australia carried a typical Eastern Pacific haplotype (Lo27). However, in the bycatch and ghost net samples, there was evidence of unidirectional movements each model are in Table  S4. All available one-sample summary statistics in DIYABC were used. One million datasets were simulated for each scenario, and their posterior probabilities were assessed through logistic regression using the best 10,000 (1%) simulations.
Olive ridley microsatellite population structure and demography In total 285 individuals from 12 globally distributed olive ridley sites, of which 11 were rookeries and one was a feeding ground, were genotyped (Table  S1 ). Almost all loci were polymorphic in the sampled populations. On average, 95% of the individuals were genotyped entirely, but a dataset of 227 individuals (including 11 from the EP feeding ground) that presented at most one missing marker was used for DAPC and STRUCTURE analyses. Similar to the mtDNA results, Atlantic olive ridley populations had lower diversity values than those of the East-Pacific and Indian-West Pacific (Table  S1 ). The olive ridley STRUCTURE analysis showed K = 3 or K = 2 with the highest likelihood depending on the method (Fig. S4 ). Nonetheless, there was clear geographic differentiation between ocean regions (Fig.
Population structure The highly significant olive ridley mtDNA structure between ocean regions (Figs. 1  and 2 , Table  S5 ) corroborates previous results from a limited dataset of 80 shorter sequences, and suggests high female philopatry at this level  (Bowen et al. 1997). In contrast to findings in loggerheads  (Shamblin et al. 2014 ) and hawksbills  (Leroux et al. 2012) , the longer mtDNA control region sequences (~ 653 vs. ~400 bp) did not provide substantially higher resolution in detecting new population structure; instead, the results were similar to those reported previously  (Bowen et al. 1997; Shanker et al. 2004 ). We found that only one of the 732 olive ridley mtDNA rookery sequences corresponded to haplotypes from a different ocean region (an EP haplotype found in Australia), showing that female single-generation dispersal between ocean regions is rare, a pattern observed in most sea turtles  (Reid et al.
Acknowledgements We thank the  Fundação Projeto TAMAR  for providing samples and overall support, and colleagues from the  Laboratório de Biologia Genômica e Molecular-PUCRS , especially  Cladinara Roberts Sarturi , for technical assistance. The  Sackler Institute for Comparative Genomics at the American Museum of natural History , as well as the  College of Staten Island of the City University of New York , are acknowledged for their support. The African samples (except for  Guinea-Bissau ) were collected and analyzed thanks to a  European Union Marie Curie Training and Mobility of Researchers.
Context. The technique of stellar occultations, greatly enhanced by the publication of the Gaia data releases, permits not only the determination of asteroid size and shape, but also the retrieval of additional, very accurate astrometry, with a possible relevant impact on the study of dynamical properties. The use of Gaia as reference catalogue and the recent implementation of an improved error model for occultation astrometry offer the opportunity to test its global astrometric performance on the whole existing data set of observed events, dominated by minor planets belonging to the main belt. Aims. We aim to explore the performance on orbit accuracy brought by reducing occultations by stellar positions given in Gaia's Data Release 2 (DR2) and Early Data Release 3 (EDR3), exploited jointly with the new occultation error model.
As a large majority of the occulted stars are today in the Gaia data releases (exceptions can exist due to the marginal incompleteness of the catalogue, especially for bright stars), the resulting asteroid astrometry (hereinafter occultation astrometry) is very accurate. In  Spoto et al. (2017) , we showed the impact of Gaia DR1 (Gaia Collaboration 2016) on the occultation astrometry, finding that, even with this preliminary accuracy, some orbits derived from data of occultations only on the Minor Planet Center (MPC) website 1  can already be of better quality with respect to orbits obtained from all available data (mainly ground-based CCD imaging). The gain in accuracy can reach one order of magnitude or more, depending on how many occultation events are available for a given object. It should be noted that this result  A&A 658, A73 (2022)  was just a proof of concept on the capability of Gaia astrometry to make occultations self-consistent in terms of orbital precision, but it was still very preliminary both in terms of the stellar accuracy provided the mission, and the extent of the analysis of the results.
Gaia DR2 (Gaia Collaboration 2018a), represents a major step with respect to Gaia DR1 and the first to reach the domain of accuracy and completeness expected from Gaia with the inclusion of parallaxes and proper motions as a major step forward in the exploitation of occultation astrometry. Gaia EDR3 (Gaia Collaboration 2021) goes further in terms of data cleanness, precision and absence of systematic errors. With respect to Gaia DR1, a full order of magnitude improvement on star positions has been realised. This advantage has now been exploited in several situations, but two extreme examples can be considered emblematic: the TNO Arrokoth  (Buie et al. 2020) , the second target of the NASA New Horizons mission, for which the occultation was fundamental to the successful fly-by, and, at the opposite end of the distance scale, (3200) Phaethon, which is the target of the JAXA/DESTINY+ mission, with a whole set of occultations successfully predicted and observed over the year 2019 2.
With this work, we intend to fill this gap. In the process, we also investigated the statistics regarding orbit accuracy when occultation astrometry is used jointly to other astrometric measurements. This is an essential step to obtain the best possible orbits to study the NEA impact risk and secular orbit changes induced by the Yarkovsky effect. In this context, we want to clarify the role of occultation astrometry with respect to asteroid astrometry obtained by Gaia itself, as occultations may represent the only technique capable of extending the ultra-accurate astrometry of asteroids collected by Gaia itself, by collecting data of equivalent quality beyond the duration of the mission. This possibility further stresses the importance of having a clear view of the statistical properties of occultation astrometry.
Another point of interest is that the use of the stellar astrometry by Gaia is sometimes not straightforward as several quality indicators and error sources appear in the tables, which can be relevant in specific cases. We assessed the role of the most relevant indicators in the frame of occultation astrometry. Our article is organised as follows. We first describe (Sect. 2) the general properties of the data set of the available occultations, from which the properties of the associated error model derive. In Sect. 3, we discuss the role of the uncertainties related to stellar astrometry and the use of quality indicators in the Gaia data releases. We then determine an orbital fit to the occultation data and extract their residuals (Sect. 4). We conclude by discussing the quality of the astrometry with respect to Gaia and the challenge of combining asteroid occultation data with the large amount of astrometry available from the ground (Sect.
Data properties and error model for the occultations Main belt asteroids are the most represented population in the data set of the currently available occultation astrometry  (Herald et al. 2020) , so they constitute a relevant statistical sample to assess its performance. Observations of stellar occultations are regularly updated and distributed through the Planetary Data System  (Herald et al. 2019 ) and (with more frequent updates) through the database maintained by the same author and associated with the Occult package 3. They are also submitted to the MPC. However, currently, not all the available information is accessible through the MPC (most notably, the error model). We thus made use of the full astrometric output available directly from the data reduction. The whole past record of occultation astrometry was recently reprocessed by  Herald et al.
The error on the star position is then summed quadratically to the error of the occultation. This procedure is also explained in  Herald et al. (2020). The orbital fit is obtained by the OrbFit 5  software tool and optimised in order to take into account the complete error model for occultations. In the process, the normalised χ 2 value of each observation is used to reject outliers, defined by χ 2 > 10. Fig. 2. Statistics of the along-track (AL) error plotted against the year of the observations. It incorporates several effects, including timing errors, uncertainties due to size and shape models (particularly relevant for single-chord events, which represent 61% of the total). Relevant features are the high dispersion before the late 90s, and the overall consistency afterwards. The AL error (two middle panels) is provided both in absolute values (mas) and in units of the asteroid radius.
For reference the first row repeats the plot with the number of occultations for each year. To show the jump in precision brought by the recent Gaia data release, we compare the performance of Gaia EDR3 on occultation astrometry, to the pre-Gaia situation when several catalogues were used to reduce both occultation results and ground-based measurements. The striking difference is illustrated by Fig. 3 , showing the distribution of the residuals to the best fitting orbit for all objects. The left panel shows the statistics on post-orbital fit residuals obtained for occultations. The fit considers all optical astrometry available, as provided by the MPC database, for the 1616 asteroids that have occultation data. The total number of occultations used is 5773. For archive data, OrbFit uses a new error model that is also being tested under this updated version, which estimates the uncertainty of observations based primarily on year of observation, observatory code, star catalogue used and asteroid magnitude.
The related study  (Lindegren 2018 ) defines different levels of RUWE thresholds as a function of the degree of cleanness desired in a sample of stars. The conventional threshold that appears to be optimal is RUWE = 1. 4, adopted here for the plot in Fig. 3  (right panel). RUWE turns out to be more reliable in general than other quality indicators taken separately, proposed in the Gaia archive. duplicated_source is a flag, set when the star could potentially have a secondary source deserving a special data reduction (that has not been applied in the currently available releases. Potentially (but not systematically, this can lead to biases in the astrometric solution, of unknown amounts). In principle, RUWE and duplicated_source are derived independently during the astrometric reduction of Gaia data, but the potential duplicity indicated by duplicated_source for a source can bring an increase to its RUWE value.
Orbital fit and residuals We then exploited the residuals computed by the orbital fit of occultations alone, reduced with the stellar astrometry by Gaia EDR3. For this task, we selected occultations of stars with RUWE < 1. 4, starting from the year 2000. By using this filter, we eliminated a very small fraction of the sample where, as mentioned above, observations are less reliable and homogeneous. We then selected objects that have at least four occultation events, as a minimum to grant a meaningful comparison with asteroids that have a larger number of occultation events. This left us with 512 objects available for analysis, with the number of occultation events for a single object reaching a maximum of 26. Of course, a very small number of astrometric data points, when just a few occultations are available, does not mean that the best possible orbit is obtained.
This last issue is in striking contrast to the existing observation record that we used for comparison, including all other astrometric data, which are in general less accurate as they refer to a mix of astrometric catalogues and present systematic differences that cannot be completely suppressed. Up to now, Gaia DR2 and Gaia EDR3 have been used rather interchangeably in occultation prediction, without a clear, quantitative view of the improvement brought by the latter in the astrometric quality. It is possible to better appreciate the evolution of accuracy by Fig. 9 , where the ratio of the semi-major axis uncertainty (same appearing on the vertical axis in Fig. 8 ) computed with the two data sets is presented. The distribution is centred around unity with a peak around 0. 9, but the skewed distribution shows that a majority of semi-major axis uncertainties present an improvement, in some cases by a large factor.
For the goal of orbital fitting, we can thus consider that the current error model is appropriate. We now have all the elements required to provide a quantitative, statistical support to the idea that stellar occultations are capable to produce astrometry at the level of what Gaia can do for Solar System objects (i. e. mas-level epoch astrometry) and that occultations can provide a good continuation to Gaia in terms of astrometric precision even after its mission is over. We illustrate this conclusion, without loss of generality, by adopting the point of view of traditional astrometry, and considering its performance as a function of the number of available photons. Data obtained from Gaia obey this law with an optimal performance around G ∼ 12-13, and a degradation for decreasing brightness.
Only 3. 2% of the occultations involving a star fainter than the asteroid (representing 17% of the whole occultation data set) have a timing error larger than errors associated to size and shape. Such errors of course diminish with smaller asteroids so both the average and the dispersion decrease, as expected, until they surpass Gaia in performance around V ∼ 16. 3 for the AL direction. Although the idea of 'doing better than Gaia' can appear surprising in itself, we stress here that there is no contradiction when the quantities that compared are correctly interpreted. In fact, on one hand we have the Gaia DR2 astrometry of asteroids for each epoch. On the other hand, we have the occultation astrometry based on Gaia EDR3 stellar astrometry. The latter is obtained from the combination of all the epochs of observation of each star, and, as a consequence, it is intrinsically better than for asteroids.