dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with threshold based method The automatic identification of the three near-earth regions had already been attempted by  Jelínek et al. (2012)  with the use of thresholds on the magnetic field magnitude and the density. Nevertheless, the quality of the classification has never been evaluated nor its adaptation to non-equatorial data. Figure  7  represents the 2D histogram of B and N p for THEMIS B, TC1 and Cluster 1 on the periods on which we labelled our different datasets. We divided these parameters by the corresponding solar wind density and the IMF that we obtained from the shifted OMNI data. At first sight, one can easily distinguish three main regions that are separated with the solid red lines for the three missions. Nevertheless, these linear boundaries have been set manually and we can not ensure these could the best choice for the three missions.
To evaluate the quality of the classification, we computed the TPR and the FPR for the three missions and for varying boundary lines. We then used these values to compute the AUC that is shown in the table 2. Once again, we notice a lower AUC in the case of Cluster which is consistent with the difference we have between equatorial and polar orbits as explained in the previous section. Additionally, even if the boundaries plotted in the Figure  7  seem to provide a decent separation between the three regions, the AUC is lower than the one we obtained with the gradient boosting. Which indicates our model performs better in classifying the three regions by setting more flexible boundaries while requiring less time than the one required to set the thresholds used in the Figure  7. The same kind of histogram gets messier with a much less obvious transition from the magnetosheath to the solar wind and the addition of the moon's wake as shown with the Artemis data in the Appendix C.
Magnetopause catalog In the case of the magnetopause, we define a crossing as a 1 hour interval that contains as much magnetosheath points as magnetosphere points. We then elaborate a complete magnetopause crossing by running our model trained in section 2 on the data provided by THEMIS A, B, C, D and E spacecraft. To gain time in the construction of the crossings and because we do not expect any magnetopause crossing in these regions, we restricted ourselves to the dayside, dawn and dusk operation phase and we removed the parts of the orbit in which the spacecraft was far away in the solar wind (X GSM > 15Re). The   3. And all of the magnetopause lists can be found here: https: //github. com/gautiernguyen/in-situ_Events_lists. Given that our detection method has been evaluated on large parts of orbits, the high quality of the classification is made with regions where the spacecraft is not expected to cross a boundary.
In these regions, the algorithm is less likely to hesitate on its prediction. On the other hand, it is more probable it hesitates on the predictions made close to the boundaries. Consequently, we have to ensure the classification is still of decent quality. Figure  8  represents the ROC we have on the classification between magnetosphere and magnetosheath points for THEMIS B, Cluster 1 and Double Star for the subset of our test set that lies in the proximity of a magnetopause or shock crossing. These predictions have been obtained with a model that has been trained with  the complement part of the dataset, i. e. the subset that excludes the proximity of the crossings. Even if the AUC is lower than the one we obtained in the previous section, its still high value indicate the good quality of the classification when a spacecraft arrives close to the magnetopause and thus our capacity of building crossings from the prediction made by our model.
Bow shock catalog We define a bow shock crossing event as 10 minutes interval that contains as much magnetosheath points as solar wind points. We then run the models we trained for the different missions detailed in Section 3 on the same dataset we used in the case of the elaboration of the Magnetopause crossing catalog. The total number of obtained crossings can is once again summarized in Table  3  and the bow shock lists can also be found at https://github. com/gautiernguyen/in-situ_Events_lists. can be found in the Appendix B and the high value of the AUC coupled to the probability distribution we have in Figure  11  convince us that the greatest part of the detected events are actually bow shock crossings. The spatial distribution of the crossings with a probability higher than 75% in the GSM XZ, XY and YZ planes is shown in method has even been tested on night side oriented missions for which we obtained the same quality of prediction after the addition of the moon's wake as an additional class and the spacecraft position as an additional feature.
Having proved this adaptability, we could also think of using the method on the data of additional near-Earth missions such as MMS, IMP8 or ISEE. The classification could even be enhanced by the consideration of additional regions like the Ion foreshock. Such lightweight algorithms could eventually be taken onboard of upcoming missions to automatically select the data of interest and thus automatically decide of the data that should be stored and kept for further analysis. Moreover, the method does not use the specificity of being in the near-Earth and could then also be adapted to other planetary missions in the solar system. We used this method to elaborate one of the largest existing magnetopause and bow shock crossings catalogs that will be automatically enlarged with the increasing quantity of data.
principle of the gradient boosting algorithms is as follows: at first, we train a decision tree, another class of machine learning based on multiple decisions over the values taken by the features of the dataset, by minimizing the false predictions made over the training set. Additional decision trees are iteratively used to model residuals of the predictions obtained from the previous tree, until the maximum of trees or sufficiently small residuals are reached. The final ensemble of trees is used to model the regions and make predictions on yet unseen data points giving as an output, the probability of belonging to each class. The https://doi. org/10. 5194/angeo-2019-149 Preprint. Discussion started: 5 November 2019 c Author(s) 2019. CC BY 4. 0 License.
Acknowledgements. While submitting this paper, we came aware that Olshvesky et al. have also discussed how to detect plasma regions in the data of MMS using a 3D Convolutional Neural Network Olshevsky et al. (2019). 20 https://doi. org/10. 5194/angeo-2019-149Preprint. Discussion started: 5 November 2019 c Author(s)  2019. CC BY 4. 0 License.
Code and data availability. THEMIS data are accessible via the NASA Coordinated Data Analysis web (https://cdaweb. sci. gsfc. nasa. gov/ index. html/). Cluster and Double Star data are accessible via the Cluster and Double Star Science archive (http://csa. esac. esa. int/). All of our trained algorithms and labelled dataset can be found here https://github. com/gautiernguyen/in-situ_Events_lists.
Additional requirements of the managing board: As indicated in the 'How does it work?' section and in the code of conduct, please make sure that: -Data are available to readers, either in the text or through an open data repository such as Zenodo (free), Dryad or some other institutional repository. Data must be reusable, thus metadata or accompanying text must carefully describe the data. -Details on quantitative analyses (e. g. , data treatment and statistical scripts in R, bioinformatic pipeline scripts, etc. ) and details concerning simulations (scripts, codes) are available to readers in the text, as appendices, or through an open data repository, such as Zenodo, Dryad or some other institutional repository. The scripts or codes must be carefully described so that they can be reused. -Details on experimental procedures are available to readers in the text or as appendices.
Additional requirements of the managing board: As indicated in the 'How does it work?' section and in the code of conduct, please make sure that: -Data are available to readers, either in the text or through an open data repository such as Zenodo (free), Dryad (to pay) or some other institutional repository. Data must be reusable, thus metadata or accompanying text must carefully describe the data. -Details on quantitative analyses (e. g. , data treatment and statistical scripts in R, bioinformatic pipeline scripts, etc. ) and details concerning simulations (scripts, codes) are available to readers in the text, as appendices, or through an open data repository, such as Zenodo, Dryad or some other institutional repository. The scripts or codes must be carefully described so that they can be reused.
Study population NutriNet-Santé is a French, ongoing, web-based cohort launched in 2009 aiming to study the associations between nutrition and health as well as the determinants of dietary behaviours and nutritional status. This cohort has been previously described in detail. 30 Participants aged over 18 with access to the internet have been continuously recruited from the general population since May 2009 by means of large multimedia campaigns. Questionnaires are completed online on a dedicated website. Participants are followed by using an online platform connected to their email address. The NutriNet-Santé study is registered at ClinicalTrials. gov as NCT03335644.
Data collection At inclusion, participants completed a set of five questionnaires related to sociodemographic and lifestyle characteristics (eg, date of birth, sex, educational level, smoking status, number of children),  31  anthropometry (height, weight),  32   33  dietary intakes (see below),  34   35 36  physical activity (validated seven day International Physical Activity Questionnaire),  37  and health status (eg, personal and family history of diseases, menopausal status, drug use including hormonal treatment for menopause and oral contraceptives). Weight was collected every six months. The web-based self administered anthropometric questionnaire was compared with a traditional paper questionnaire,  33  and reported weight was also validated against weight measured with a calibrated scale (BC-418MA, TANITA, Tokyo, Japan) by trained investigators in a validation study on a subsample,  32  showing high consistency.
Other web-based questionnaires used to collect baseline characteristics of the participants (eg, sociodemographic and lifestyle data) have also been tested against traditional paper questionnaire,  31  showing very high consistency between the two methods, and even a lower proportion of missing or abnormal values in the web questionnaire, owing to integrated controls, mandatory fields, and conditional skip patterns. Other methodological studies were conducted to test for the reliability of the data declared online by the participants and also observed very high levels of consistency. 38 For instance, the consistency between declared information on sex, date of birth, and department of birth of a sample of participants and the first digits of their social security number was checked. Regarding dietary data collection, at baseline and every six months (to vary the season of completion), participants were asked to fill three non-consecutive validated web-based 24 hour dietary records, randomly assigned over a two week period (two weekdays and one weekend day in order to take into account the variability of the diet during the week).
Biological sampling and clinical examination Participants in the NutriNet-Santé study were invited, on a voluntary basis, for a visit in one of the local centres specifically set up for biological sampling and clinical examination, including bioimpedance measurements, in each region (83 hospital centres). A visceral fat index was calculated with a calibrated impedance body composition analyser (BC-418MA, TANITA, Tokyo, Japan), based on total body and regional % fat estimations which were previously validated against dual-energy X-ray absorptiometry. 41 ore generally, fat estimates by body composition analyser were validated against dual-energy X-ray absorptiometry in several studies. 42 43 The visceral fat index ranges from 0 to 60 units, and is considered excessive when over 12 units. These biological and clinical data were collected for 19 772 participants of the cohort, among which 15 637 pertained to the study sample of the present study.
Case ascertainment Health events were self declared through a yearly questionnaire, a specific check-up questionnaire (every three months), or at any time through a specific interface on the study website. Each declaration of incident cancer was controlled by a physician from the study team who contacted participants and asked them to provide any relevant medical records. When additional information was needed, we contacted the patient's physician, or hospital, or both. All medical data was reviewed by a committee of physicians. The NutriNet-Santé cohort is linked to medico-administrative databases of the national health insurance system (SNIIRAM databases) and to the French national cause specific mortality registry (CépiDC). Based on these databases, we could complete information regarding health events and deaths, thereby limiting any potential bias owing to participants with cancer who might not report their disease to the study investigators.
Statistical analysis We defined baseline as the date of inclusion in the cohort (when participants finished completing the set of baseline questionnaires on diet, physical activity, health, sociodemographic characteristics, and anthropometry). We included 101 257 participants without cancer at baseline who provided at least two valid 24 hour dietary records during their first two years of follow-up. Appendix 3 shows the study flow. Appendix 4 compares included with excluded participants. We performed variance reduction by calculating usual daily beverage intakes with the method proposed by the US National Cancer Institute (SAS macros %MIXTRAN followed by %INDIVINT) to correct for within person and between person variability and to deal with zeroes (non-consumption). 44 We calculated daily dietary intakes in this prospective analysis by taking into account all 24 hour dietary records available during the first two years of each participant's follow-up and used these as baseline usual dietary intakes.
We used the Multiple Imputation by Chained Equations method by fully conditional specification (15 imputed datasets) to handle missing data in covariates for the following variables: smoking status (0. 1% of missing data), level of education (6. 3%), physical activity level (14. 0%), height (0. 6%), and BMI (0. 6%). 45 We estimated weight gain during follow-up as the percentage of weight gain between inclusion of participants and the last weight declared to date (excluding weight data reported during the two years preceding a patient developing cancer). We defined sex specific quarters of consumption for each type of sugary drink. Participants were included at different entry dates, had different follow-up durations, and censored data. Studied cancers were overall, breast, prostate, and colorectal cancers (the most frequent cancer sites in the cohort), and lung cancer in the secondary analyses.
As a sensitivity analysis, another approach to take into account competing risk was tested using cause-specific Cox models, providing cause-specific hazard ratios. We tested the proportional hazard assumption of the Fine and Gray model by using rescaled Schoenfeld- type residuals computed along with the SAS macro %PSHREG as described by Kohl and colleagues. 49 he corresponding P values were obtained by testing Pearson correlations between residuals and log (follow-up duration) (appendix 5). In continuous models, the increment for the different beverage types was adapted according to the distribution of their consumption. An increment of 100 mL/d was adapted for sugary drinks, but it was excessive for artificially sweetened beverages regarding their range of consumption in this study (as shown in distribution plots, appendix 6).
Aims. We take advantage of the second data release of the Gaia space mission and the state-of-the-art astrometry delivered from very long baseline interferometry observations to revisit the structure and kinematics of the nearby Taurus star-forming region. Methods. We apply a hierarchical clustering algorithm for partitioning the stars in our sample into groups (i. e. , clusters) that are associated with the various molecular clouds of the complex, and derive the distance and spatial velocity of individual stars and their corresponding molecular clouds. Results. We show that the molecular clouds are located at different distances and confirm the existence of important depth effects in this region reported in previous studies. For example, we find that the L 1495 molecular cloud is located at d = 129.
2017 Joncour et al. , 2018)). The morphology and kinematics of these gaseous clouds and filaments have been clearly characterized in recent years based on CO surveys and extinction maps (see, e. g. ,  Ungerechts & Thaddeus 1987; Cambrésy 1999; Dame et al. 2001; Dobashi et al. 2005; Goldsmith et al. 2008) , and increasing progress is being made to constrain the three-dimensional structure and stellar kinematics of the individual clouds. However, until recently many studies have been hampered by the lack of accurate data for a significant number of stars, in particular stellar distances and spatial velocities, which could provide us with valuable information about the star formation history in this region. Distances to individual stars are, in general, derived from trigonometric parallaxes; until recently there were very few parallaxes for Taurus stars.
Doing so, we found proper motions and trigonometric parallaxes for 411 stars from our initial sample. Radial velocities in Gaia-DR2 are available for only 34 stars in our sample, so we searched the CDS/SIMBAD databases  (Wenger et al. 2000)  to access more radial velocity measurements. Our search in the literature, which was as exhaustive as possible, was based on  Wilson (1953 ), Hartmann et al. (1986 ), Hartmann et al. (1987) ,  Herbig & Bell (1988) ,  Reipurth et al. (1990) ,  Duflot et al. (1995) ,  Mathieu et al. (1997 ), Wichmann et al. (2000) ,  White & Basri (2003) ,  Muzerolle et al. (2003) ,  Gontcharov (2006) ,  Torres et al. (2006) ,  Kharchenko et al. (2007) ,  Scelsi et al. (2008) ,  Nguyen et al. (2012), and Kraus et al. (2017). In addition, we also used the more recent measurements collected with the Apache Point Observatory Galactic Evolution Experiment (APOGEE) spectrograph  (Kounkel et al.
Hierarchical Mode Association Clustering (HMAC) has the advantage of elucidating the relationship (and hierarchy) among the various clusters in the sample when compared to other commonly used clustering algorithms, for example k-means (MacQueen 1967) and DBSCAN  (Ester et al. 1996). It is used in this study to investigate the structure of the Taurus molecular cloud complex and to reveal important clues to the star formation process in this region. In the forthcoming analysis we use the Modalclust package  (Cheng & Ray 2014)  which implements the HMAC algorithm in R programming language. We run HMAC from the phmac routine using a number of smoothing levels (i. e. , bandwidths) defined as described below, and use the hard. hmac function to access the cluster membership of each star at a given clustering level.
We construct our dataset for the clustering analysis with HMAC using only the five astrometric parameters (α, δ, µ α cos δ, µ δ , ). Many stars in our sample are still lacking radial velocity measurements, thus they will be included only in a subsequent discussion (see Sect. 5) to refine our results. In the first step of our analysis we rescaled the five astrometric parameters so that the resulting distributions have zero mean and unit variance. We obtain the same results using rescaled and nonrescaled parameters, and we therefore decided to work with the non-rescaled astrometry as given in the original sources. The hierarchical clustering is performed in a bottom-up manner using a sequence of bandwidths σ 1 < σ 2 <. < σ L (in all P. A. B. Galli et al. :  Structure and kinematics of the Taurus star-forming region from Gaia-DR2 and VLBI astrometry dimensions) that need to increase by a sufficient amount to drive the merging of the existing clusters at level l with l = 1, 2,.
Removing outliers in the individual clusters HMAC has shown to be able to detect the most extreme outliers in our sample which have been grouped into clusters of one single data point. However, we still note the existence of a more dispersed population of stars in some clusters that clearly extends beyond the limits of the molecular clouds (see, e. g. , cluster 7 in Fig. 4 ). In this section we revise the membership status of these sources and reject potential outliers in the individual clusters. In this context, we use the minimum covariance determinant (MCD, Rousseeuw & Driessen 1999) method that is a robust estimator of multivariate location and scatter efficient in outlier detection. Our dataset used for the clustering analysis is stored in an n × p data matrix X = (x 1 , x 2 ,. , x n ) t with x i = (x i1 , x i2 ,.
Two points are worth mentioning here before comparing our results with that study. First, the two studies had distinct objectives which explains the different strategy employed to explore the Gaia-DR2 data in the Taurus region. Luhman (2018)  performed an extensive analysis to improve the census of Taurus stars by refining the sample of known members and identifying new candidates. In this context, the sources were not filtered (as done in the present study) to minimize as much as possible the rejection of potential members of the Taurus region. On the other hand, we decided to apply the RUWE selection criterion in the present study, which is a more conservative approach to filter the stars in the sample. This procedure is likely to remove some bona fide members of the Taurus region, but at the same time it minimizes the number of stars with discrepant measurements in the sample due to a poor fit of the Gaia-DR2 astrometric solution or to non-membership.
Conclusions We used in this study the best astrometry available to date by combining Gaia-DR2 data with the VLBI results delivered by the GOBELINS project to investigate the three-dimensional structure and kinematics of the Taurus star-forming region. Both projects return consistent results for the targets in common and complement each other in this region of the sky. We applied a hierarchical clustering algorithm for partitioning the stars in our sample into groups with similar properties based on the stellar positions, proper motions, and parallaxes. Our methodology allowed us to identify the various substructures of the Taurus region and discuss their relationship (i. e. , hierarchy). We found 21 clusters in our sample at the lowest level of the hierarchical tree and a number of outliers that exhibit discrepant properties.
Acknowledgements. We thank  Alvaro Hacar ,  Estelle Moraux , and  Isabelle Joncour  for helpful discussions that improved the manuscript. This research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  682903 ,  P. I. H. Bouy ), and from the  French State  in the framework of the  "Investments for the future" Program ,  IdEx Bordeaux , reference  ANR-10-IDEX-03-02. L. L. acknowledges the financial support from  PAPIIT-UNAM  project  IN112417 , and  CONACyT. G. N. O. -L. acknowledges support from the  von Humboldt Stiftung. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www.