dataesr/scientific-paragraphs-categorization huggingface dataset:
Measurements at  MSY (Montseny)  and  MSA  (Montsec) stations were supported by the  MINECO (Spanish Ministry of Economy, Industry and Competitiveness)  and  FEDER  funds under the  PRISMA  project ( CGL2012-39623-C02/00 ) and under the  HOUSE  project ( CGL2016-78594-R ), by the  MAGRAMA (Spanish Ministry of Agriculture, Food and Environment)  and by the  Generalitat de Catalunya  ( AGAUR 2014 SGR33 ,  AGAUR 2017 SGR41  and the DGQA). Measurements at IZO (Izaña) were supported by the  AEROATLAN  project ( CGL2015-17 66229-P ), co-funded by the  Ministry of Economy and Competitiveness of Spain  and the  European Regional Development Fund. Station KOS (Košetice)  is supported by the  Ministry of Education, Youth and Sports of the Czech Republic  within the project to support the national research infrastructure ACTRIS -participation of the Czech Republic ( ACTRIS-CZ -LM2015037 ).
Measurements at  PUY (Puy de Dôme ) were partly supported by  CNRS-INSU, University Clermont-Auvergne, OPGC  and the french CLAP programme. The PAL (Pallas) station acknowledges  KONE Foundation ,  Academy of Finland  (project no. 269095  and no. 296302 ). CHC (Mt Chacaltaya ) station received support from  Institut de Recherche pour le Développement (IRD)  under both  Jeune Equipe  programme attributed to LFA and support to ACTRIS-FR programme. CHC received grants from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ). Marco Pandolfi  is funded by a  Ramón y Cajal Fellowship  ( RYC-2013-14036 ) awarded by the  Spanish Ministry of Economy and Competitiveness. The authors would like to express their gratitude to  David Carslaw  and  Karl Ropkins  for providing the OpenAir software used in this paper (Carslaw and Ropkins, 2012;Carslaw, 2012).
The database of the CANU system (813 women), in the manner that it is made available on the Brazilian Society of Angiology and Vascular Surgery website, only offers the ability to search by name or postcode and does not provide a list of specialists with their respective electronic addresses. This database could not therefore be used for distribution of the questionnaires. Question 1 on the instrument requested the participant to provide their name and Regional Medical Council registration number (CRM) and questionnaires for which the name provided did not correspond to the CRM, duplicate questionnaires, and questionnaires with false names were excluded. As such, Question 1 is not used in analysis of the results and no data that could identify the informants are used. The questionnaire was developed based on similar studies by Umoetok et al.
RESULTS At the end of 30 days of data collection, 106 of the 293 questionnaires sent out had been completed, five of which were excluded because the data provided in answer to Question 1 were invalid. Just one of the Chart 1. Questionnaire "Women in vascular surgery: a brief analysis of the Brazilian profile". 101 valid questionnaires reported a nationality other than Brazilian (Colombian). The category with the greatest number of responses to the third question ("Which state do you practice in?") was São Paulo (n = 43), followed by Bahia (n = 11). Figure  1  illustrates the distribution of participants across Brazil's five administrative regions. Question 4 classified female vascular surgeons by age group, revealing that 46. 5% were 25-35 years of age, 43. 6% were 36-45 years old, and less than 10% were over the age of 46.
The global extrapolation produced weights displaying strong seasonal and geographical patterns, which translated into spatiotemporal differences between the ET weighted and simple average ET products. However, the uncertainty of the weights after the extrapolation remained large. Out-sample prediction tests showed that the tower data set, mostly located at temperate regions, had limitations with respect to the representation of different biome and climate conditions. Therefore, even if the local weighting was successful, the extrapolation to a global scale remains problematic, showing a limited added value over the simple average. Overall, this study suggests that merging tower observations and ET products at the timescales and spatial scales of this study is complicated by the tower spatial representativeness, the products' coarse spatial resolution, the nature of the error in both towers and gridded data sets, and how all these factors impact the weights extrapolation from the tower locations to the global landscape.
Nonetheless, satellite-based ET products also show large discrepancies which are put in evidence when inter-compared and evaluated against in situ flux networks  (Jimenez et al. , 2011; Mueller et al. , 2011; McCabe et al. , 2016). Far from discouraging the use of these ET data sets, the inter-product differences have been perceived as an opportunity to foster research and find new means to combine these data sets in an optimal manner. So far, these efforts have ranged from simply averaging a number of ET products  (Mueller et al. , 2013)  to more complex approaches, such as weighted averages  (Hobeichi et al. , 2018) , fusion algorithms where the original ET products are combined to reproduce flux observations  (Yao et al. , 2017) , or integration methodologies that seek consistency between ET products and related products of the water cycle  (Aires, 2014; Munier et al.
Note that for the early-stopping technique training and validation subsets contain data from the same stations. So, if the out-sample technique is also applied, the data from the removed station are no longer part of the training or validation subsets during the cross-validation. Note that as tower measurements were masked for rainy intervals (see Sect. 3. 2), the interception loss of the modelled ET is not evaluated. Therefore, only the sum of soil evaporation and transpiration is compared with the tower data and weighted. To derive the total ET merged product, an estimate of interception loss should also be provided, either by (1) assuming that GLEAM, PT-JPL, and PM-MOD interception losses are equally uncertain and adding their average to the weighted soil evaporation and transpiration, or (2) adding just one of the individual model interception losses, if there are reasons to believe that the selected one is less uncertain.
Tower data The FLUXNET 2015 synthesis data set (http://fluxnet. fluxdata. org/, last access: 8 July 2018) is used to obtain pointbased measurements of evaporation (referred to as tower ET), and it is processed as in  Martens et al. (2017)  to retain only high-quality data appropriate to evaluate the evaporation estimates. Starting from the original time resolution (generally 30 min or 1 h), the processing involves (1) masking measurements using the originally provided quality flags; (2) masking measurements for rainy intervals, only leaving observations if both the global precipitation product and the local measurements (if available) do not indicate precipitation (as eddy-covariance measurements are less reliable during precipitation events); and (3) aggregating to daily values if more than 75 % of remaining sub-hourly data exists for a given day.
, 2000)  and the energy balance residual approach  (Amiro, 2009)  being the most frequently adopted. Corrected fluxes are typically preferred over the original uncorrected observations, but these corrections imply the need for surface radiation and soil heat flux measurements, which are not routinely measured at all stations. At the sites where they are available, the FLUXNET 2015 data set offers a test product containing a corrected version of the heat fluxes based on the Bowen ratio approach, i. e. assuming that the measured Bowen ratio is correct. For the 84 stations selected here, 26 do not have Bowen ratio corrected (BRC) fluxes. For the remaining 58 stations, the relative mean difference between the original and BRC latent heat fluxes averaged over all stations is 6. 1 %, with a maximum value of 16.
Limiting the study to stations with a relatively large number of years could minimize this drawback, but it would severely reduce the number of towers, so this filtering has not been ap- plied. For instance, if we only derive weights for towers with at least 4 years of data, half of the towers would have been removed. Notice also that due to the masking of the tower data the 61 consecutive daily estimates required to estimate our temporally varying weights (see Sect. 2. 2) are generally not all available. Therefore, in the case of the tower data we set a minimum threshold of 15 daily values within the 61-day running window for the error to be estimated. Most stations have weights for nearly all days, but in a few stations there are recurrent gaps. A clear example is the tropical BR-Sa3 station, where the frequent rainy episodes complicate the derivation of the weights.
Ancillary data Because the substantial mismatch between the size of the model grid cells and the tower footprint is likely to result in representativeness errors, ancillary data sets are required to characterize the spatial homogeneity of the grid cells where the stations are located. Two data sets are considered: the MODIS Land Cover Type product MCD12Q1 at a native resolution of 500 m, and the Terra MODIS Vegetation Continuous Fields product MOD44B, available at a spatial resolution of 250 m. A homogeneity index (I h ) is constructed as follows: I h = 1 2 Fgt IGBP + 1 2 (1-| Fg bare -Ft bare | -| Fg herb -Ft herb | -| Fg forest -Ft forest |), (8) where Fgt IGBP is the fraction of MCD12Q1 500 m cells included in the 25 km model grid cell containing the tower and having the same IGBP land cover than the model cell, Ft bare , Ft herb , and Ft forest are, respectively, the bare, herbaceous, and forest fractions of the MOD44B 250 m cell containing the tower, and Fg bare , Fg herb , and Fg forest are the same fractions but calculated for the entire 25 km model grid cell where the tower is situated.
Weights extrapolation The number of stations used in this merging exercise is certainly limited in terms of covering different biomes and climatic conditions. Hence, the ability to represent the full distribution of ET across time, space, and biomes is questionable. This is verified here by out-sampling the NN training data set in two different ways. In the first test all stations are included in the tower data set, i. e. the standard configuration used to produce the global WA-merger. Before training the NN, 15 % of the days at each station are randomly masked from the training data set, and the prediction statistics are derived over this independent subset. In the second test, the station where the prediction will be checked is entirely removed from the training data set, i. e, the weights for that station are derived using a NN that did not include that station in the training phase (i.
e. leave-one out cross-validation). A box plot summarizing the correlation and RMSD between the station weights and the weights predicted by the NN for these two tests is presented in Fig. 12. The results clearly show that the correlation and RMSDs between the predicted and the original weights at the stations degrades notably when stations are fully removed from the training data set. This implies that the global extrapolation of the weights will be quite uncertain for conditions not sampled in the available tower data set. For some stations, the outsampling from the training data set does not have a large effect, because the mapping between the predictors and ET can still be approximated from the relationship presented by other stations. This is for instance the case for the Canadian forest stations CA-NS1-7 (results not shown).
However, for other stations, the statistics are good when predicted with the standard data set, but poor with the one-station-removed data set, indicating that the particular conditions of those stations are not well represented in the out-sampled data set. This happens for stations such as US-Wi4 (forest with a snowy winter and warm humid summer) and CN-Dan (grasslands with a polar tundra climate). Finally, there are also stations where statistics are rather poor in both tests, indicating that a link between the model inputs and the related output error could not be established. This is the case for stations such as IT-Col (deciduous broadleaf forest with temperate climate) or MY-Pso (tropical forest). This guarantees that the extrapolation of weights to areas with similar conditions will be very uncertain, even if those conditions were represented in the tower data set.
An additional test to check the representativeness of the tower data set is conducted by globally extrapolating the weights with each of the previous 84 NNs trained without one station, and then checking the variability of the predicted weights. For the conditions well represented in the training data set, it is expected that removing one station will only result in slight changes in the extrapolated weights. However, for regions that are poorly represented, a slightly different data set is likely to result in substantially different weights. This is illustrated in Fig. 13 , where a weight variability index is displayed. The index is calculated by (1) estimating for each global cell the annual standard deviation of the GLEAM, PT-JPL, and PM-MOD weights, normalized by the sum of the absolute annual model weights, and (2) averaging this standard deviation over the three models.
To facilitate its display in Fig. 13 , it has been scaled to span the range 0-1. Overall the variability is larger in the Southern Hemisphere than in the Northern Hemisphere, which is expected given that all stations but two are situated in the Northern Hemisphere. The smallest variability in the weights coincides with the regions where the database is more representative, namely the US, central Europe, and some parts of Asia, suggesting a bias in the tower data set linked to the specific location of the towers selected. The variability in tropical regions, where only three stations are part of the database, is in general larger than for the previous regions. The largest variability occurs over the very dry regions, a regime poorly represented in the tower data set as shown in Fig. 1. While a poor extrapolation of weights is not critical over very dry regions, given their low ET values, uncertain weights over the very humid regions is more of a concern due to their typically large ET values and their significance for the global mean ET.
Here this is an issue for GLEAM and the merged products (as they include GLEAM), but not for PT-JPL and PM-MOD as they do not use precipitation data as input. As such, WorldClim precipitation data are also used in addition to MSWEP in these comparisons (GLEAM was forced with MSWEP; see Sect. 3. 1). The mass balance of a catchment implies that the space and time integration of P -Q should equal the ET integrated over the same space and time, if one assumes that the changes in soil water storage within the catchment are small compared with the cumulative volume of ET, P , and Q. The longer the period, the more valid this assumption becomes. Here, the mean 2002-2007 ET estimates from GLEAM, PT-JPL, PM-MOD, and the merged products are calculated per catchment. The basin P -Q estimate is then calculated using the Q and P data described in Sect.
3. 3. We only select catchments for which the P -Q data record is available for a minimum of 3 years in the 2002-2007 period, to assure some common period between ET and P -Q. In addition, to reduce noise in the basin-integrated ET estimates, only basins with a catchment area containing at least 3 × 3 cells of the 25 km resolution gridded estimates are included in the comparison. This results in 685 basins, 75 % of them situated in the Northern Hemisphere (i. e. showing a similar geographical bias as the tower data set). Catchments are further divided into three groups of 243, 295, and 147 basins based on the aridity index (AI, basin potential ET over the basin P ) taking values in the intervals AI < 1, 1 < AI < 2, and AI > 2. Scatter plots showing the correspondence between P -Q and ET are given in Fig.
Both mergers were also compared with the ET inferred from water balance calculations in different catchments across the globe, and similar correlations and RMSDs were obtained, with only slightly better results for the WAmerger over wet basins. Several limiting factors for the merging exercise are identified, some of which could be informative for other initiatives aiming to blend ET data sets. A longer study period can give access to more in situ data and extend the in situ data set to less represented regions. This would clearly help the global extrapolation of the weights. In addition, the mismatch between the spatial resolution of the towers and the products is still an issue, despite the fact that here other error sources were deemed to be more dominant. The impact of the mismatch in spatial resolution is expected to be minimized as ET data sets move towards finer spatial resolutions.
Critical for the success of the merging scheme is the adequate characterization of the uncertainty of the individual products, and finding an effective method to extrapolate the weights from the tower space to the global landscape. The latter seems challenging, and given the difficulties found here, alternatives should be considered. A possibility could be triple collocation  (Yilmaz et al. , 2012). This technique would require two new global ET data sets independent from the products that need to be merged. This can be demanding, but work in that direction has already started  (Khan et al. , 2018). An added advantage of this approach will be that the tower observations could then be used as an independent evaluation set, similar to the approach carried out for some other Earth Observation products, such as the soil moisture estimates from the ESA Climate Change Initiative  (Gruber et al.
Acknowledgements. This study was funded by the  European Space Agency (ESA ) and conducted as part of the project  WACMOS-ET-Ensemble  (ESRIN contract no. 4000117355/16/I-NB ). Diego G. Miralles acknowledges support from the  European Research Council (ERC)  under grant agreement number  715254  ( DRY-2-DRY ). Joshua B. Fischer  contributed to this paper at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. The  California Institute of Technology  and government sponsorship is acknowledged. Support to  Joshua B. Fisher  was provided by  NASA 's SUSMAP, THP, and  INCA  programs, and the  ECOSTRESS mission. Kevin P. Tu , from the  Department of Ecosystem and Conservation Sciences, University of Montana , is acknowledged by providing guidance for the use of the vegetation products in this study.
Since 2015, we have been collaborating with journalists from Le Monde, France's leading national newspaper, in an R&D project named ContentCheck, focused on content management technologies for journalistic fact checking  [38]. The goal of this paper is to analyze and understand what role content management technologies, seen as data and knowledge management, information extraction, natural language processing, and their interactions, can play toward facilitating, speeding up and improving fact-checking work, whether performed by journalists or other fact-checkers. 1  We stress that we do not believe it is feasible to "automate" fact-checking or journalistic work; nor is it desirable, since journalistic content is authored by humans who understand, shape and drive the interests and knowledge of their fellow humans.
From an NLP perspective,  [63]  defines fact checking as the assignment of a truth value to a claim made in a particular context, on an ordinal scale, borrowed from the popular Truth-o-Meter scale used by fact checking web sites Politifact ("True", "Mostly True", "Mostly False", etc). It also presents a "golden standard" (fact checks) dataset of manually checked statements, against which automated checking methods can be evaluated. The recent white-paper by FullFact  [3]  defines fact checking as a four-stage process where (i) media sources are monitored, (ii) claims are spotted, (iii) claims are checked, (iv) fact checking analysis results are created and published. This definition is quite consensual among the works we discuss below; claim spotting (also termed claim extraction) and claim accuracy checking have received most significant attention.
Reference data source search To check a given claim, one needs to identify suitable reference sources against which to check, and if sources are many, select the most relevant ones. In FactMinder  [27] , the authors envision fact-checking as a manual analysis of an article (not a specific claim) published online, against a background of reference data sources. They focus on identifying, in a reference database of RDF knowledge bases and structured documents, those semantically related to the entities (people, places etc. ) present in the article to be fact-checked. The technical solution is based on (i) identifying (matching) the article entities to those in the reference RDF graph and (ii) evaluated pre-defined queries to retrieve the content "characterizing" entities of each known type (e.
Fact checkers have today at their disposal a wealth of data sources, which for simplicity and ease of use should ideally be exploited together through a single point of access. Data integration, a well-established line of data management research, aims at allowing users to interact with a set of data sources as if it was a single database. While several flavors of data integration systems have been proposed, data spaces  [22]  appear well-adapted to handling heterogeneous reference sources, independently developed, and available in disparate formats (relational data, HTML or Excel tables, CSV, RDF, JSON frequently found in open data. ). Tatooine  [8]  is a dataspace-style data integration prototype integrating querying, classification, and visualization components for data journalism and fact-checking applications.
Claim extraction Claim extraction (also called claim detection) leverages NLP technologies to extract from text, sharp (structured) claims which one may subsequently check for accuracy. An early attempt at this task in DisputeFinder  [19]  focused on identifying, in an online text, claims potentially covered by a database of reference containing disputed claims (on which contrary opinions have been stated). Phrases are transformed in bags of words through stop word removal, stemming etc. and matched against the database, both in positive mode (to see if the database backs it) and in negative mode (if the contrary is stated in the database). DisputeFinder then shows users additional information on to how or why the claim is disputed, such as related articles or argumentation graph. DisputeFinder also allowed users to add claims the believed were disputed, however a user study showed users sometimes confusing "disputed" with "false", or improperly reported claims and/or sources.
This highlights that user expertise is needed in order to contribute meaningfully. In  [35]  a formal definition of the task of context-dependent claim detection is proposed: given a topic (short sentence) and a set of relevant articles, their goal is to automatically extract concise statements from the articles that directly support or contests the given topic. The authors also provide manually labeled benchmark dataset and a classification approach based on a cascade of classifiers. Claim extraction from text feeds (tweeter, newspapers, legislative proceedings), and ultimately, audio and video streams is reported to be also a focus of FullFact ongoing work  [3] ; in particular, they are interested in automatically identifying the claims covered by prior FullFact manual fact checking work.