dataesr/scientific-paragraphs-categorization huggingface dataset:
This indicator is based on the idea that significant errors in elevation are usually related to significant errors in 2D position  [52]. It requires using a precise DTM of the area to be measured: The elevation of GNSS data is compared to the elevation at the same x, y position on the DTM. Slope is the gradient of altitude at the location derived from the underlying referential DTM. The Obstacles indicator provides an estimate of the multipath effect, one of the most influential factors in GNSS accuracy. It considers the proximity of obstacles (e. g. , within 10 m) that contribute to multipath within our test environment, namely buildings and forests. Building-and land-cover maps from an authoritative dataset are used to compute this indicator. The threshold for defining close features is related to the average precision expected from a smartphone GNSS and the precision of the reference data.
They model factors influencing GNSS measurements as described in Section 2. To compute the extrinsic indicators, three types of authoritative data coming from the topographic database (named BDTopo), produced by the French National Mapping Agency (IGN), and with metric accuracy and are used: Digital Terrain Model (DTM) raster data and building and land cover vector data. DiffEleDTM is an extrinsic indicator and assesses the accuracy of GNSS elevation on one point. This indicator is based on the idea that significant errors in elevation are usually related to significant errors in 2D position  [52]. It requires using a precise DTM of the area to be measured: The elevation of GNSS data is compared to the elevation at the same x, y position on the DTM. Slope is the gradient of altitude at the location derived from the underlying referential DTM.
The Obstacles indicator provides an estimate of the multipath effect, one of the most influential factors in GNSS accuracy. It considers the proximity of obstacles (e. g. , within 10 m) that contribute to multipath within our test environment, namely buildings and forests. Building-and land-cover maps from an authoritative dataset are used to compute this indicator. The threshold for defining close features is related to the average precision expected from a smartphone GNSS and the precision of the reference data. This could be refined with distance thresholds related to the height of obstacles. Curvature represents the convexity of slope. Vegetation represents the land cover class at the position of the point, as defined by a reference land cover map (LCM). CanopyCover states whether the point is in the forest or not, according to a reference LCM.
InBuildingWater is an indicator that identifies inconsistencies. Due to the imprecision of GNSS, some points can be located in water or on building surfaces according to an authoritative dataset. This reflects an inconsistency with normal hiking/cycling activities and is therefore a clue for an outlier. A majority of these indicators were defined by the authors of this paper to appropriately model the most important metric and geometric characteristics of the outliers. Some other indicators such as speed rate were used since they have already been successfully used in trace analytics.
Test Data Description The test area was in the Vosges Mountains (France). It was chosen for its interesting characteristics: It is a small, mountain-mixing, dense forest with different canopies and open areas. For our study, a total of 437 traces (9773 km) were downloaded from hikers and mountain bikers' websites (randoGPS, tracesGPS, visuGPS, and VTTour) sharing GNSS traces. The traces were collected while performing sports activities such as running, hiking and cycling. Since traces were collected without a data collection protocol and since no metadata were available, no distinction was made according to the transportation mode (e. g. , walking, running, or biking), and no information was available about their expected spatiotemporal resolution. The completeness and heterogeneity of this data are described below.
Points were theoretically described by, at least, 2D coordinates (World Geodetic System 1984-WGS-84), timestamps, and elevation. Among 300,000 points, 106,206 points (36. 3%) lack timestamps, and 6580 points (2. 2%) lacked elevation. Regarding the traces, 157 of them (35. 9%) had no timestamps at all, whereas 287 (65. 6%) had at least one missing timestamp. Timestamps are important information necessary for different analyses (e. g. , speed or acceleration). This random distribution of timestamps and a particularly huge number of traces with no timestamps at all (35. 9%) were significant issues when trying to use this data and assessing their quality. To evaluate the heterogeneity of crowdsourced traces, the distributions of data frequency, speed (if timestamped) and distance between consecutive points within each trace were studied.
To evaluate the heterogeneity of crowdsourced traces, the distributions of data frequency, speed (if timestamped) and distance between consecutive points within each trace were studied. Statistical tests such as Shapiro-Wilk, skewness and kurtosis (K) tests showed that speed and distance are rarely normally distributed. For example, less than 7% of traces were normally distributed for speed, and no traces were normally distributed for distance. Moreover, it was noticed that different distributions of speed values between traces existed (see Figure  5 ). Figure  6  illustrates the variation of skewness of speed values for all traces. The variety of speed distribution within each trace is illustrated by the variety of skewness estimation for all traces. This may be explained by frequent changes in the speed during a trip (e.
Detection of Outliers The detection of outliers was carried out by following four steps: (i) Defining training examples, (ii) applying a machine learning algorithm, (iii) a quantitative validation, and (iv) a qualitative validation. The first step consisted of defining training examples. Thus, a training area was selected, and each training example was manually labelled with two values: Outlier or regular point. In the sampling zone illustrated in Figure  8  (black boxes), 2342 points were selected and manually labelled as outliers or regular points. Among the total number of points, 35 (3%) points were labelled as outliers and 2265 (97%) were labelled as regular points. Clearly, the learning dataset was very imbalanced (one outlier within 66 regular points). The second step was the learning process.
As stated before, a rule-based learning algorithm was chosen to enable interactive modifications or improvements of learnt rules, which is impossible with non-symbolic classification algorithms. In addition, they are known as efficient and able to deal with examples described by numeric, qualitative, and possibly missing attributes  [53]. By using Weka software package, four different rule-based algorithms were applied: Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Projective adaptive resonance theory (PART), M5Rules, and One attribute rule (OneR). The third step was the quantitative validation. The validation of results was performed using 10-fold cross validation in order to ensure a statistically unbiased evaluation knowing the low number of examples. Validation results of the four applied algorithms are presented in the Error! Reference source not found.
Overall, the RIPPER algorithm performed best. The validation results gave both precision and recall equal to 79%, and the F1 measurement equalled 0. 79. The results confirmed the performance of the approach and learnt rules, in spite of the lack of metadata as well as the presence of missing attributes. Finally, five following rules were learnt to detect outliers (see Table  4 ). The second step was the learning process. As stated before, a rule-based learning algorithm was chosen to enable interactive modifications or improvements of learnt rules, which is impossible with non-symbolic classification algorithms. In addition, they are known as efficient and able to deal with examples described by numeric, qualitative, and possibly missing attributes  [53]. By using Weka software package, four different rule-based algorithms were applied: Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Projective adaptive resonance theory (PART), M5Rules, and One attribute rule (OneR).
Two actual methods can address this issue. The first one is to assign distinct costs to train examples to avoid the misclassification of rare classes  [57]. The second one involves re-sampling the original dataset  [58] ; rare cases may be over-sampled, or the most common cases may be under-sampled. The two methods may be combined. Other authors reported that learning based on imbalanced data is not an issue when the classes are diametrically opposed regarding their characteristics, with no class overlapping  [59] , as in our case. In our approach, oversampling outliers was done by adding some outliers outside of the main sampling area to the training data. A manual classification of a sufficient number of examples may be the main limitation of the work. In order to overcome this issue, some machine learning approaches like active learning may be considered.
Those approaches automatically choose a limited but pertinent set of examples for manual classification in order to minimize the manual classification effort. The proposed filtering methods have been applied to GNSS traces collected in mountain areas. Without the full availability of metadata, it is difficult to know exactly which sensor was used (e. g. , GPS) for each trace acquisition. The same is for the type of sports activity. Nevertheless, as a result of analysing the websites and the traces themselves, we observed that in our dataset different activities were performed. We consider that both secondary human behaviours and outlier detection are independent of a sensor type and performed activity as well as of the transportation mode (e. g. , walking or cycling). Due to the fact that the method for filtering secondary human behaviour is only a geometry-based method, it is expected to be applicable on various types of secondary human behaviour, regardless of environmental characteristics.
To reduce the bias, 77 examples of outliers beyond the sampling zone were added to the training dataset. In this way, the statistical representativeness of the examples was lost, but more examples of outliers were obtained with limited effort. ISPRS Int. J. Geo-Inf. 2018, 7, x FOR PEER REVIEW 10 of 17 (3%) points were labelled as outliers and 2265 (97%) were labelled as regular points. Clearly, the learning dataset was very imbalanced (one outlier within 66 regular points). To reduce the bias, 77 examples of outliers beyond the sampling zone were added to the training dataset. In this way, the statistical representativeness of the examples was lost, but more examples of outliers were obtained with limited effort.
Acknowledgements Adriana Consorte-McCrea ,  Ana Fernandez  and  Alan Bainbridge  would like to thank the  Futures Initiative, CCCU , without whose funding and support the symposium "Large carnivores and zoos as catalysts for biodiversity conservation: how do we engage the public in the protection of biodiversity?" at ECCB 2018, which formed the basis for the development of this paper, wouldn't have been possible. J. V. L. B. was supported by the  Ramon & Cajal program  ( RYC-2015-18932 ) from the  Ministry of Economy, Industry and Competitiveness.
Acknowledgements I am grateful to  Aroldo de Andrade ,  Chiara Gianollo ,  Paul Hirschbühler ,  Richard Ingham ,  Cecilia Poletto  and the colleagues who attended the conference Information Structure and language change at  Université de Caen in April 2018  for extensive discussions of the analysis proposed in this paper. I also thank the three reviewers of this journal for helping to bring about a number of clarifications. I remain solely responsible for any residual infelicities.
Recruitment and Participants We displayed posters and ads in select community locations in Rochester, MN, and on websites and conducted face-to-face outreach between March 5 and June 11, 2018. We recruited a convenience sample of adolescents from three community settings that serve racially and socioeconomically diverse youth and offer programs after school and on weekends. Study team members consulted with the staff at each community-based organization to determine appropriate times for recruitment and data collection. Recruitment took place four times at the Rochester Public Library and three times at the YMCA, each lasting about three hours; a local church also hosted one recruitment event. Study staff screened adolescents in person to determine their potential to participate based on the study eligibility criteria: (1) aged 12-17 years old and (2) possessing a completed written informed consent and assent document, signed by the parent or guardian and participant.
Statistical Analyses SPSS Statistics for Windows (IBM Corp) was used to analyze the data; the data were summarized using univariate and bivariate statistics. Acceptance ratings-TAS individual items and total score-were summarized for the overall sample using descriptive statistics. The association of TAS total score with sociodemographic characteristics was examined using t tests for dichotomous variables of age group (12-14 versus 15-17 years), sex (male or female), racial minority status (yes or no), meeting physical activity national recommendations (yes or no), and depression history (yes or no). One-way analysis of variance (ANOVA) was used to examine the association of total TAS score with BMI, categorized as underweight, normal, or overweight, and estimated household income status, categorized as low, medium, or high.
We did not specifically ask about the use of alternatives to cigarette smoking, including e-cigarettes, hookahs, or vaping, whose use may have been more prevalent  [35]. To reduce participant burden, we used self-reported data about height and weight, but these data may be unreliable. Although reasonable for a pilot study, the sample size was insufficient for conducting multivariate analyses on the association of participant characteristics and TAS score; thus, findings are exploratory. Furthermore, we assessed acceptability using a brief, mock introductory coaching session and did not measure acceptability while delivering a specific exercise intervention. It is possible that the robotic trainer will not translate into an acceptable, effective, or useful mode of engaging adolescents in physical activity.
Acknowledgments This study was made possible by a  Clinical and Translational Science Awards  grant from the  National Center for Advancing Translational Sciences , a component of the  National Institutes of Health  (facilities grant number:  UL1 TR02377 ; training grant number:  TL1 TR 002380 ). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the National Institutes of Health. We wish to acknowledge the support of the  Mayo Clinic Community Engagement in Research Advisory Board , the  Rochester YMCA , and the  Rochester Public Library. We appreciate the manuscript support given from  Michelle Pearson  and  Denise Chase.
We complement these extensive efforts by investigating the degree to which socio-technical factors facilitate the visibility and emergence of authority among contrarian claims-makers  36. To address this literature gap, we focus our analysis on a group of 386 prominent contrarians, denoted both individually and collectively by CCC. We compare these CCC with 386 prominent scientists active in CC research, denoted hereafter by CCS. These experts in CC science serve as an objective measurement baseline for juxtaposing visibility in the media with authority in the scientific domain. To operationalize this integrative comparison, we collected two large datasets through 2016, comprised of ∼200,000 CC research articles from the Web of Science (WOS) and ∼100,000 English-language CC media articles from the Media Cloud (MC) project  37.
Results Dataset construction focusing around individuals. CC research and media coverage have grown steadily over the last quarter century, drawing on a range of actors from the scientific, public, media, and policy domains  36. According to MC project  37  data reported in Fig. 1 , the term "climate change" is currently used in approximately 10 4 media article sentences per week, roughly 100 times as much as the term "climate skeptic", a broad term that collectively refers to contrarians and denialists, and also conventional scientific skeptics who are driven by more legitimate motives for dissent  28, 42. For this reason, we focus on a select set of contrarians who have publicly and repeatedly demonstrated their adamant counterposition on CC issues  12  -as extensively documented by the DeSmog project (DeSmogblog.
We then collected ∼200,000 CC research articles from the WOS database, from which we selected the 386 highest cited scientists (denoted by CCSs). These prominent scientists, many are pre-eminent CC experts with distinguished careers spanning several decades, serve as a size-balanced comparison group. Supplementary Fig. 1  lists the 100 most highly cited CCSs in our sample. We provide more detailed information on the derivation of the CCC and CCS groups in the "Methods" section and Supplementary Information Note 1. Arriving at two lists of prominent CC actors, we then downloaded article metadata covering roughly ∼100,000 CC media articles from the MC project  37  , a public database of media article metadata (see "Methods"), which facilitates large-scale data-driven studies at the intersection of society, politics, and media  43.
For each member (hereafter distinguished by the index i) of the CCSs and CCCs, we counted how many media articles (given by M i ) and in which media sources did he/she receive visibility (hereafter we distinguish media sources by the index s). It is important to note that MC data does not include social media posts (e. g. , from Facebook and Twitter) and thus represents publicly visible hybrid content (web-only and dual print-web content) from a wide range of content producers, reflecting varying levels of production effort and quality. We also accounted for article multiplicity, i. e. , articles within the MC dataset with different hyperlinks and unique article MC identifiers but with the same title and media source (see "Methods" for more details on this MC article disambiguation procedure).
Accordingly, we find that only 224 of the 386 CCC have a single publication in our WOS dataset. Thus, in our analysis drawing on scientific publication data, we proceed by comparing just the 224 published CCC with a size-balanced subset comprised of the 224 most-cited CCS. We denote these two subsets by 224CCC and 224CCS, respectively. Proceeding at the group level, we tallied the total number of unique publications (i. e. , counting a publication coauthored by two or more members of a given group only once). Figure  3a  shows the disparity in scientific productivity, with the 224CCC subset publishing 3367 scientific articles (on average 15 articles per individual). Conversely, the 224CCS subset published Likewise, we tallied the total citations received by each publication set. Figure  3b  shows an even larger disparity in citation impact, with 224CCS collecting roughly 7.
Juxtaposing authority and visibility at the individual level. To test whether the discrepancy in scientific authority and media visibility is also present at the individual level, and not the result of just a few outliers driving group totals, we disaggregated the WOS and MC data into individual profiles. Figure  4a  compares the article count M i between individuals of the same rank within their respective groups. CCCs are consistently more visible in the media relative to their CCS counterparts; this disparity persists even when comparing visibility within the prominent select-30 media sources. Figure  4b  shows the total number of media articles (M i ), the total number of WOS publications (P i ), and the total number of citations (C i ). Shown together, this representation highlights the relatively small intersection between the two groups.
Among the sources that balance according to sourcing type, the Guardian, NYT, and LAT most commonly mention individuals from each group, whereas WP and FOX tend to incorporate individual contributions from each group. Co-visibility in the media. It was unfeasible to apply the content analysis to the entire dataset, and so we turn to network analysis to identify additional relational patterns of co-visibility within groups and across their media interface. To proceed, we first merged the sets of CCS and CCC media articles. Whereas M i counts the total number of media articles for individual i, the covisibility M ij ≤ M i counts the number of articles that feature both individuals i and j. Combining the matrix elements M ij calculated for all pairs of individuals, we construct the symmetric covisibility matrix M.
3a ). We also analyzed the directed citation flow between any given pair of individuals that occurs when a publication p a authored by individual a cites a publication p b authored by individual b. To be specific, we counted the citation linkages a ∼ p a → p b ∼ b that connect any pair of authors, where a ∼ p a indicates that individual a is an author of publication p a , and p a → p b indicates that publication p a cited p b. Figure  7b  shows the resulting interpersonal CC citation network, comprised only of scientists who gave or received at least one citation within our WOS dataset. As such, there are only 168 CCCs connected within this citation network. We again used the Louvain algorithm  48  to identify groups of nodes that are densely connected together, representative of coarse research communities.
Our individual-centric approach also provides insight into the structural properties of the consensus-contrarian interface. Analysis of the interpersonal citation network reveals that 224CCS are cited by the remaining CC community 17 times more frequently than 224CCS. Even accounting for productivity differences, we find that 224CCC cite 224CCS 20 times more frequently than in reverse. Similar analysis of the media co-visibility network shows that 52% of the associations are between CCCs, whereas only 12% are between CCSs, an additional disparity pointing to manifest organizational differences, such as the contrarian echo chamber illustrated in Fig. 6. In summary, our work contributes to recent computational social science efforts  7, 34, 38, 49, 56  by leveraging new opportunities in largescale data collection and analysis  37  to clarify both the individual and collective properties of complex socio-technical systems  57.
Related research on the emergence of polarization around critical yet controversial socio-political issues  51, 58  , the impact of new media on the public  59  , and the spread of inaccurate information  16, 17, 43  will together provide important guidance on how to improve the effectiveness of CC communication  14, 15, 21, 32, 60, 61. Indeed, despite the challenges posed by new media, there are also new opportunities. One relevant example that borrows from the post-publication peer-review system in science is the new media tool climatefeedback. org  62 , which allows expert scientists the opportunity to annotate, grade, and correct inaccurate CC information published in the media  63. Methods MC data. We collected a dataset of 121,729 unique print articles, online articles, and blog posts on CC from 01 January 2000 to 01 October 2016 from 7126 unique media sources from the MC project, an open data project hosted by the MIT Center for Civic Media and the Berkman Klein Center for Internet & Society at Harvard University (https://cyber.