dataesr/scientific-paragraphs-categorization huggingface dataset:
22; 95% CI: -0. 05 to 0. 46). Highlighted species show mismatches between mitochondrial genetic diversity, nuclear genetic diversity, and IUCN Red List conservation status (LC, least concern; NT, near threatened; VU, vulnerable; EN, endangered; CR, critically endangered). For example, the endangered Galápagos penguin has lower nuclear genetic diversity than other species in our data set, but mitochondrial diversity comparable to several other species, and the Marbled murrelet, also endangered, has relatively high mitochondrial and nuclear diversity. diversity estimates separately. We were interested in whether genetic diversity predicted red-list status and whether red-list status was informative for species genetic diversity. A strong correlation between genetic diversity and red-list status would suggest that either may be used to inform the other.
This result highlights that there was no strong tendency for markedly low genetic diversity to be associated with CR, EN, or VU species. The range of genetic diversity in LC species encapsulated genetic diversity across all other red-list categories for all markers and taxa (Figure  3 ), and LC was the most common status (84% of mtDNA data, 65% of microsatellite data, 69% of WGS data). This scenario of high model accuracy but low predictive power is known as the "accuracy paradox"  (Fernandes et al. , 2010). These results demonstrate that a species' red-list status cannot be predicted from species-level genetic data and red-list status cannot be used as a surrogate for species' risk of genetic erosion in the absence of genetic data. Thus, red-list status alone is not useful for informing decisions related to the conservation of genetic diversity of individual species.
In these types of comparative models, low is defined relative to other species, whereas conservation geneticists are primarily concerned with low contemporary genetic diversity compared with past levels of genetic diversity in a species. Thus, questions about decline or risk of genetic erosion cannot be fully addressed when comparing genetic diversity snapshots across species. Because the nature and causes of genetic diversity loss are variable across genetic markers, populations, species, and species' ranges, interspecific comparisons of red-list rankings and average species genetic diversity are not particularly informative for identifying species at risk of genetic erosion. Genetic diversity declines are ideally assessed with intraspecific data sampled over time, but unfortunately, this is difficult to do at scale (but see  Leigh et al.
A WAY FORWARD Red-list rankings are used extensively for conservation planning, often at the species or regional level. Defining conservation status helps to prioritize actions for species at the highest risk levels (CR and EN) and identify at-risk regions or key biodiversity areas  (Hoffmann et al. , 2008). The red-list process of compiling information from experts also allows analysis of regional and global threats to species  (Rodrigues et al. , 2006). Change in the red-list index over time is a useful global indicator of biodiversity loss  (Tittensor et al. , 2014)  (see  Fraixedas et al. [2022]  for critique of other aspects of the IUCN Red List). Given its focus on demographic change, it was important to test whether red-list status captured genetic diversity, which would remove the need to develop additional tools to assess genetic erosion and enable the use of genetic data to inform red-list assessment.
We found that the IUCN Red List, on its own, did not reflect the genetic diversity patterns of individual species and thus is not reliable for informing policy aimed at conserving and recovering genetic diversity. Direct genetic diversity assessments, in combination with the use of well-verified proxies or indicators of genetic diversity  (Hanson et al. , 2017; Hoban et al. , 2020 Hoban et al. , , 2022) )  and other conservation tools, are needed to assess species' vulnerability to genetic erosion. Moving forward, it is necessary to shift away from policy tools that were not designed to evaluate or protect genetic diversity when the goal is to conserve genetic diversity  (Hoban et al. , 2022). Nuclear genetic data are becoming available for increasing numbers of species  (Leigh et al. , 2021) , enhancing the ability to look more deeply into genetic diversity status within and among populations of species  (Hoban et al.
Genetic status could be similarly incorporated into IUCN Green Status assessments  (Stephenson et al. , 2019). Standardized collection and storage of these data would lead to a global database of species' genetic statuses and make IUCN Red List assessments a more multifaceted resource. In short, a vital and feasible first step is to start recording and storing measures of species genetic status during the red-list assessment process, even if this information is not immediately used to inform the red-list status, which would require approval of IUCN members. The rapid collection and use of genetic, genomic, and proxy metrics in a coordinated way across species is essential to help the scientific community inform conservation decision-making. Producing and aggregating these data, however, requires considerable effort.
Sequence data can be stored in centralized repositories (e. g. , GenBank); however, similar programmatically accessible databases are not available for other genetic markers, most notably microsatellites, which remain in wide use. To ensure accessibility to researchers and practitioners, proxy data should be aggregated in centralized databases that can be expanded and updated over time to capture temporal trends, ideally as part of the IUCN Red List or Green Status. For all data types, improving metadata standards (minimally to include the location and date of sampling) is imperative  (Leigh et al. , 2021) , and recent advances such as the GEOME database  (Deck et al. , 2017)  are opening the door to more accessible, searchable, and reusable genetic data. The ongoing development of genetic indicators as well as a suite of agreed-upon genetic "essential biodiversity variables"  (Andersson et al.
ACKNOWLEDGMENTS We thank  M. Thorstensen ,  D. Leigh ,  A. Korpach , and the  Population Ecology and Ecological Genetics group  at the  University of Manitoba  for their feedback on the manuscript. I. P. -V. was supported by the  U. S. Geological Survey Powell Center for Synthesis and Analysis. C. J. G. was supported by a  Natural Sciences and Engineering Research Council of Canada   Discovery Grant. This work was conducted as a part of the Standardizing, Aggregating, Analyzing and Disseminating Global Wildlife Genetic and Genomic Data for Improved Management and  Advancement of Community Best Practices Working Group  supported by the  John Wesley Powell Center for Analysis and Synthesis , funded by the  U. S. Geological Survey. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U.
g. , data relating to catches, species distribution, population structure and abundance) and thus hampering both the assessments of relevant species and stocks and effective management. 10958649, ja, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/jfb. 15466 by Uit The Arctic University Of, Wiley Online Library on [01/06/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Mis-reporting of the Dipturus species is linked to the superficial physical similarities between species, with some landings being labelled as 'skate' or 'common skate' (or the equivalent local name [e. g. , 7,12, Barreau, pers comms 2021]). Although current fishing regulations prohibit the retention and landing of D.
ICES DATRAS (hereafter as To ensure only records of D. intermedius were retained, data points from the combined resulting database were filtered to include only specimens ≥160 cm, as D. batis (the smaller of the 'common skate' complex) has a maximum length of ca. 150 cm  [11, 28]. Where length information was missing, weight data were converted into length, using formulae given by McCully et al. [40] , for the 'common skate' complex. Following the size-based selection, a total of 83 records were retained from the DATRAS dataset, out of a total of 3529 records. In some of the surveys carried out by the Cefas (Cefas; GOVL 2014-2017) including surveys on a commercial fishing vessel which specifically targeted both species, D. intermedius were identified according to the morphological features described by Iglésias et al.
[11]. These features include the colour of the iris, the wing blotches, the direction of the lateral tail thorns, the interspace between dorsal fins, and the shape of the teeth [see 11]. These latter records were considered to be verified and included in the final data set regardless of size. To further validate species ID, each record obtained from IMR were confirmed by contracted fishermen, who were responsible for the data collection. Datasets were collated in R, version 4. 0. 2 [41] , and visualised using ArcGIS Pro® GIS software  [42]  ICES reference layers used to classify point locations were downloaded online  [43]. Additional data on the distribution of D. intermedius were obtained through email correspondences with relevant personnel from several museums, universities and research institutions within the purported range of the 'common skate' complex.
Molecular analysis Cytochrome c oxidase I (CO1) sequences of North Atlantic Dipturus species were downloaded from GenBank (see Table  S2  for full list of species and sequence accession numbers). This gene region has previously been shown to be useful to uncover cryptic diversity in North Atlantic Dipturus  [11, 44, 45] , and other species-complexes  [46]. In each case, species names were checked against WoRMS  [3]. Multiple sequence alignments were carried out using both CLUSTALV  [47]  and MUSCLE  [48] , checked manually in Aliview  [49]   S2 ). Prior to phylogenetic analyses, jModeltest v2. 1  [50]  was used to determine the most appropriate nucleotide substitution model for the alignment data. Hasegawa-Kishino-Yano (HKY) was the best fit model chosen by jModeltest with the best Bayesian Information Criterion score.
'DATRAS', the Database of Trawl Surveys managed by International Council for the Exploration of the Sea (ICES)) occurrence records for 'D. intermedia', 'D. batis' and 'D. flossada' (note the outdated nomenclature) were extracted using the icesDatras package [version 1. 2-0; 39] from all available surveys. Relevant metadata including species length (HL) and associated geographical locations (HH) for each data point were combined into a single database. For the additional sources including the Centre for Environment, Fisheries and Aquaculture Science (Cefas), the Institute of Marine Research (IMR), Inland Fisheries Ireland (IFI) and The Scottish Sea Angling Conservation Network, equivalent information were obtained by direct email request (SSACN; Table.
Occurrence records, as obtained from fisheries survey databases, angling associations, Genbank and relevant researchers in the field confirmed a consistent presence of D. intermedius in the Northeast Atlantic, concentrated in the northern North Sea and the northern and western seaboards of Scotland, Northern Ireland and Ireland, as far south as the Celtic Sea (Fig 2, see also Fig C supplementary material). Dipturus intermedius was also recorded as far north as Southern and Western Norway, with exceptional/rare reports in the French Bay of Biscay, Portugal and the Azores. Data points were concentrated in coastal shelf seas at depths of 50-600 m.
Acknowledgements  10958649 , ja, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/jfb. 15466by  Uit The Arctic University Of, Wiley Online Library on  [ 01/06/2023 ]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions)on  Wiley Online Library  for rules of use; OA articles are governed by the applicable Creative Commons License AG, JT and NP are supported through the  European Union 's  INTERREG VA Programme (Environment Theme ) SeaMonitor project managed by the  Special EU Programmes Body (SEUPB). Project  ID IVA5060. SL is currently undertaking a PhD studentship funded by the  Department of Agriculture , Environment and Rural Affairs. This paper results from the skate regional working group funded through the SeaMonitor project. We thank the laboratories and scientists of the various trawl surveys who have provided data through DATRAS, and  Joana Silva  (Cefas), for reviewing earlier versions of the manuscript.
intermedius Flapper skate, Present Shark and Ray ID cards, Flapper The Shark Trust, grey skate skate ID guide 2020 Irish waters D. intermedius Flapper skate Present An inventory of elasmobranch Varian, 2011 databases for Irish waters Mediterranean D. batis Common skate, Present The Conservation Status of Dulvy et al. complex Norwegian skate, Sharks, Rays and Chimaeras in 2016 grey skate the Mediterranean Sea Mediterranean D. batis Grey skate Present Elasmobranchs of the Bradai et al. and Black Sea complex Mediterranean and Black Sea 2012 Mediterranean Not listed na na The Mediterranean Large Mancusi et al. and Black Sea Elasmobranchs Monitoring 2020 (MEDLEM) database Mediterranean Not listed na Questionable Species diversity, taxonomy and Serena et al. and Black Sea distribution of Chondrichthyes 2020 in the Mediterranean and Black Sea North Sea D.
Multiple COVID-19 diagnosis methods based on information collected from patients have been proposed during the global pandemic crisis, with the aim of providing medical staff with quick diagnosis tools to efficiently plan and manage the limited healthcare resources. In general, these methods have been developed to detect COVID-19 positive cases from a particular combination of reported symptoms, and have been evaluated using datasets extracted from different studies with different characteristics. On the other hand, the University of Maryland, in partnership with Facebook, launched the Global COVID-19 Trends and Impact Survey (UMD-CTIS), the largest health surveillance tool to date that has collected information from 114 countries/territories since April 2020. This survey captured various individual features including gender, age groups, selfreported symptoms, isolation measures, and mental health status, among others.
Specifically, most of these methods propose simple rules or build machine learning models that evaluate a set of individual attributes to determine whether the corresponding user is a COVID-19 positive case. However, a consistent comparison framework that evaluates the performance yielded by the different methods is missing since the generated models and the corresponding conclusions are presented and evaluated using different, relatively small, datasets that are heterogeneous in size and type. On the other hand, in April 2020, the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, launched the largest global health surveillance platform to date  [20]. More precisely, this project daily stored, on a daily basis, the responses provided by a set of Facebook invited users about different topics related to the COVID-19 pandemic such as the presence of symptoms, RT-PCR outcomes, and the vaccination acceptance, among others.
This data collection instrument was available in 56 languages and it recorded tens of millions of responses from 114 countries or territories worldwide. Furthermore, each recorded survey contains the information on most of the symptoms considered by previously reported detection methods, and this information provides a ground truth to benchmark the different techniques in a consistent way. In this paper, we conduct a consistent comparison of different methods that detect the COVID-19 positive cases from a combination of features collected from surveys. To this end, we take into account the information included in the UMD-CTIS records as the benchmark framework to evaluate the performance of the various prediction methods using records extracted from five countries: Brazil, Canada, Germany, Japan, and South Africa.
Results In this work, we aim at comparing the performance of various COVID-19 diagnosis methods using the data provided by a unified survey instrument (UMD-CTIS) extracted from five countries: Brazil, Canada, Germany, Japan, and South Africa. Table  2. F 1 score and its 95% confidence interval for the selected countries for 2020, in %. Furthermore, we compare the performance yielded by the methods under test for two non-overlapping periods: 2020 and 2021. Notice that this study extracted the datasets among the respondents who reported both at least one symptom and the result of a test done in the previous 14 days. As can be seen in   1  shows the information about other individual features such as gender, age groups, the number of registered symptoms, and the set of symptoms stored by the UMD-CTIS survey for each country and each year.
4. Additionally, the scatter plots of the precision versus the sensitivity is shown in Fig. 2 , where the ensemble averages and the CIs of the precision for the different countries and for 2020 and 2021 are shown in Tables  3  and 6 , respectively, in the supplementary material A. These scatter plots also include the Pareto front illustrating the set of methods with the best trade-offs between precision and sensitivity. First, it is worth to notice that the TPR of the study population is a parameter to be considered to evaluate the performance of the various detection methods. More precisely, the TPR affects performance metrics such as the F 1 score and precision that assess the performance of the detection method for the positive class. In essence, prediction rules will likely detect more active cases and therefore will exhibit larger F 1 scores and precision values, when the TPR of the dataset is high.
For example, as can be seen in Tables  2, 3 , and 4, the F 1 scores generated by the different detection techniques for the countries with high TPR are, in general, larger than scores obtained for the countries with low TPR. Indeed, when assuming that all cases are positive, the F 1 scores yielded for the countries with high TPR are at least two times larger than those obtained for the countries with low TPR. Similarly, as can be observed in Tables  3  and 6  in the Supplementary Material A, the precision values outputted by different methods for the countries with high TPR are larger than those obtained for the countries with low TPR. Hence, this comparative study considers the TPR of every dataset as a source of bias that can introduce confounding. One may compare the performance of various methods and select the best model for detecting COVID-19 active cases.
UMD-CTIS Survey We perform a consistent comparative study of various COVID-19 active case detection methods from data subsets provided by the UMD-CTIS survey  [24]. More precisely, since April 23, 2020, Facebook worldwide users were invited to participate in the UMD-CTIS survey by displaying a special banner at the top of the user page. Users who accepted the invitation were moved to a web-survey platform, where potential participants must report age > 18 and consent of data use before responding to the survey. The survey instrument, designed by the University of Maryland, consists of a web-based questionnaire collecting information on gender, age groups, symptoms, COVID testing, isolation, and vaccination, among others. These questionnaires were translated into 56 languages for 114 countries and territories.
For every country and period, we build a dataset by picking the answers reporting a lab test done in the last 14 days (the survey does not collect the test type) and one or more potential COVID-19 symptoms, i. e. , this comparative study selects the tested and symptomatic cases. We select symptomatic cases because feature-based predictive methods typically aim at finding the combination of symptoms that detects infected people. In addition, we choose the tested individuals with the aim at obtaining the ground truth sample set that allows to quantitatively evaluate the performance of the different methods. Since questionnaires contain categorical data, we apply a binary encoding (dummy coding) to each response such that every potential choice aggregates a column to the dataset with binary values (0 or 1).
This leads to datasets with 201 features (attributes, columns, or variables) for 2020, and the datasets have between 431 and 452 columns for 2021 depending on the selected country. For each dataset, this study evaluates the performance of the various COVID-19 active case detection methods. To this end, our study divided every dataset into 100 partitions. For each trial, 80% of the dataset rows (questionnaires or samples) were randomly selected as training samples, and the remaining 20% were used to test the various methods.
Menni This method performs logistic regressions to build the optimal COVID-19 classification model for a set of individual features such as age, gender, loss of smell and taste, cough, fatigue, and loss of appetite  [10]. The building and evaluation of the classification model used a dataset extracted from a symptom tracker based on a smartphone app launched in the United Kingdom and the United States in March 2020. Specifically, this method was evaluated over responses from 2, 618, 862 participants voluntarily recording their symptoms. In this study, the best classification model according to the Akaike information criterion (AIC) was described as where symptoms represent binary variables. More precisely, every feature coded as 1 reports the presence of the symptom, while each variable coded as 0 indicates its absence.
The gender variable also stands for a binary variable, the one-value indicates a male, and the zero-value represents a female. Afterward, this method identified a COVID-19 active case whether e x 1+e x ≥ 0. 5. It is worth noting that UMD-CTIS data did not register the skipped meal variable. Therefore, we modified the Menni method by computing the x score with the skipped meals variable fixed to zero. This approach is labeled as Menni_1. Furthermore, we followed the procedure reported in  [10]  to build the logistic regression model from individual features available in our dataset (Menni_2). In other words, we built a logistic regression model that considers the features: age, gender, loss of smell and taste, cough, and fatigue.
Roland This study performs logistic regression analysis to build a classification model based on five symptoms: loss of taste and smell, body aches, fever or chills, shortness of breath, and sore throat  [12]. This method uses a dataset extracted from an anonymous electronic survey publicized with ads on social networks (Facebook, Twitter, Reddit, and Nextdoor) from March 31 to April 10, 2020. Specifically, the Roland method was evaluated from a dataset provided by 620 participants of whom 339 reported COVID test outcomes. This work built a stepwise logistic model whose training set was obtained by randomly extracting 25% of the rows belonging to the COVID-tested individuals. The remaining rows were used to examine the performance of the classification model.