dataesr/scientific-paragraphs-categorization huggingface dataset:
Bulk densities and compositions Combining the mass measurements (see Section 4. 3) with the modelled radii (1. 206 ± 0. 039  ⊕ , 2. 355 ± 0. 064  ⊕ , and 2. 133 ± 0. 058  ⊕ ), we find planet densities of 4. 97 ± 0. 94 g cm -3 , 2. 60 ± 0. 26 g cm -3 , and 2. 72 ± 0. 33 g cm -3 , respectively. This implies that the density of the smaller inner planet is significantly higher than that of the two larger, outer planets. We now place the mass and radius measurements of the planets orbiting L231-32 into context and compare them to composition models. In Figure  4 , we show a mass-radius diagram for small planets ( < 3  ⊕ and  < 10  ⊕ ). The properties of L231-32 are shown, along with those of other planets for which planet masses and radii are determined to better than 20%. To do so, we made use of the TEPcat 10  database (Southworth 2011) as a reference, which includes both masses measured through RVs and As can be seen in Figure  4 , L231-32b is consistent with a composition track corresponding closest to an Earth-like rocky composition (i.
This work In Figure  7 , we show a period-radius diagram of this sample of planets. A distinct paucity of planets is observed at around  = 1. 6--1. 8  ⊕. We determine the location and slope of this valley using support vector machines (SVMs), following the same procedure as outlined in  Van Eylen et al. (2018). With SVMs we can determine the so-called 'hyperplane of maximum separation' between two populations, which in this case will correspond to an equation of the radius valley as a function of orbital period. To do so we use SVC (support vector classification) as part of the Python machine learning package scikit-learn. As initial classification, we consider planets to be on the lower side of the M dwarf valley if they are below the known location of the radius valley for FGK stars from  Van Eylen et al.
Finally, as L231-32's planets orbit in near-resonances, the observation of further transits, from ground or in future TESS observations, may result in the detection of transit timing variations, which could independently constrain the planet masses and further refine the values reported here. the French National Research Agency in the framework of the Investissements d'Avenir program (ANR-15-IDEX-02), through the funding of the "Origin of Life" project of the Univ. Grenoble-Alpes. M. E. acknowledges the support of the DFG priority program SPP 1992 "Exploring the Diversity of Extrasolar Planets" (HA 3279/12-1). This work is made possible by a grant from the John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation.
ACKNOWLEDGEMENTS We are grateful to  Megan Bedell  for discussions about the extraction and precision of ESPRESSO radial velocity observations and  Laura Kreidberg  for discussions about HST observations of this system. 31  International Center for Advanced Studies (ICAS)  and  ICIFI (CONICET) ,  ECyT-UNSAM ,  Campus Miguelete ,  25  de  Mayo y Francia , ( 1650 ) Buenos Aires, Argentina Part of this work is done under the framework of the KESPRINT collaboration (http://kesprint. science). KESPRINT is an international consortium devoted to the characterisation and research of exoplanets discovered with space-based missions. Based on observations made with ESPRESSO on the  Very Large Telescope ,  ESO  observing programs  0102. C-0456 ,  1102. C-0744  and  1102. C-0958  and with HARPS at the ESO 3.
DATA AVAILABILITY This paper includes raw data collected by the TESS mission, which are publicly available from the Mikulski Archive for Space Telescopes (MAST, https://archive. stsci. edu/tess). Observations made with ESPRESSO on the Very Large Telescope and with HARPS at the ESO 3. 6m telescope (programs 0102. C-0456, 1102. C-0744, 1102. C-0958, and 1102. C-0339) are publicly available at the ESO archive (http://archive. eso. org/). All processed data underlying this article are available in the article and in its online supplementary material. The code underlying E M analysis for planets c & d's minimum predicted mass from the photoevaporation model are available at https: //github. com/jo276/EvapMassTOI270.
riparia females (Fig. 1 ). Boxplots (Fig. S5  and S6, SM1) indicated considerable overlap in copulatory organ measurements, while somatic characters showed greater divergence. Leg length (PatTib) showed the largest differences between populations in both species but failed the Bartlett test for homoscedasticity and could not be utilized for further analysis. The ANCOVA analysis suggested significant differences in the variances of male copulatory organ lengths in both species when taking into account body size. In females the size differences in copulatory organs were mostly insignificant. Additional t-test with unequal variances mostly corresponded with the ANCOVA (Table  S6 ). ddRADseq assembly and datasets. Many datasets with various parsing were assembled to examine the effect of the amount of missing data and to investigate how clustering at different values of m affected the p-distance results (Table  1 ).
The amount of missing data ranged from 15. 6 to 82. 3%. Percentage of SNPs was between 4. 3 and 9. 6 for different datasets. Rip_pal datasets included all sequenced specimens with outgroups, Pal_mon datasets (except _m10c85) included available specimens from the monticola group, Rip datasets included only  the pullata group while the Pal_mon_m10c85 dataset contained only P. palustris and P. ovtchinnikovi. The datasets Rip_m10c85 and Pal_mon_m10c85 were used for STRU CTU RE analysis to measure the extent of gene flow between populations and between closely related species. High m values were used in these analyses to diminish the proportion of missing data. A value of 85 was found to be optimal for the clustering threshold and was used for all datasets. Summary statistics for the assemblies are provided in SM2.
Phylogenetic inference, haplotype networks and species trees. Maximum likelihood trees for COI that included only specimens attempted for ddRADseq confirmed the previously reported DNA barcode sharing within the pullata group (Fig. S7, SM1 ). The same was observed in the ML COI tree that included all public records in BOLD (SM3). In the case of P. palustris, the pattern was similar, populations from Russia were clustered separately to the Finnish and Faroe Island specimens for the ddRADseq mimicking dataset (Fig. S7 , SM1). The same was observed in the ML tree built in IQ-Tree and species ID tree built in BOLD for the monticola group (SM3 and SM4). However, when building a species ID tree in BOLD for the larger dataset of the monticola group, FER and American specimens fell within one of the European clusters (SM5).
3 ). Species trees calculated using SVDquartets implemented in PAUP* showed high support for splitting distant populations into two different species. However, the bootstrap support in inner nodes remained quite low (50), thus SVDquartets did not support P. pullata as a distinctly separate species. The same was observed within the monticola group, where the split between P. palustris and the rest of the species had a bootstrap value of 50 (Fig. S8, SM1 ). Species delimitation. BIN analysis conducted in BOLD in both cases assigned Russian specimens to already known European species (P. riparia BIN: BOLD:AAF7515; P. palustris BIN: BOLD:ACH2627). Species discovery methods GMYC and bPTP returned contradictory results based on COI analysis and were largely dependent on the dataset analyzed. GMYC oversplit both species when analyzed alone or in combination with species from the same species groups.
riparia from FER and Finland belong to the same species (Fig. 3 ). The situation with P. palustris is different. STRU CTU RE results suggested that the Russian and Finnish populations had limited shared ancestry (Fig. 3 ). The mean estimated probability was higher for K3 than for K2 (Fig. S9, SM1 ), thus P. palustris populations appeared more diverged than P. riparia populations. Pairwise distance patterns in ddRADseq datasets. Intra-and interspecific p-distances varied dramatically depending on m value. The clear difference between intra-and interspecific distances was observed from m = 3 to m = 9 in the pullata group and from m = 3 to m = 15 in the monticola group (Fig. 4 ). Concurrently, p-distances between the FER and Finnish populations for both species remained within the 95% confidence interval of intraspecific distance irrespective of m.
Materials and methods Sampling. Specimens for morphological (155 specimens), COI (25 specimens) and ddRADseq (17 specimens) analyses were sampled from target populations of P. riparia and P. palustris in Finland and the Magadan Region, FER. These were coupled with representatives of pullata and monticola species groups from Europe and Asia, several other Pardosa species and outgroup species from Lycosinae subfamily [Alopecosa aculeata (Clerck, 1757) and Trochosa spinipalpis (F. O. P. -Cambridge, 1895)]. Wider sampling of species from target species groups was done by mining public records from the Barcode of Life Database (BOLD) for COI analysis (212 specimens, 14 species). Detailed information about all the specimens used in the study is in Tables S1, S2 and S3 (SM1). Specimens were collected by hand or using pit-fall traps and preserved in absolute or 70% ethanol.
riparia males, SeptLen and SeptWid-length and width of septum in females. Males (P. riparia 15 specimens from each population; P. palustris 26 from Finland, 22 from FER) and females (P. riparia 15 specimens from each population; P. palustris 21 from Finland, 22 from FER) were analyzed separately. The measurement scheme can be found in Fig. S4  (SM1) and the measurements in Tables  S4  and S5  (SM1). Principle Component Analysis (PCA) was used to analyze variation in the morphological data using RStudio with pcomp command  80  and the results were visualized with ggplot2 package  81. Each character between two populations was checked for homoscedasticity in R (bartlett. test) and compared with ANCOVA using CarLen as a covariate to control for body size variation following the same rationale as in  57.
In addition, a t-test with unequal variances was utilized as an additional comparison between measurements. To visualize overlap between measured characters boxplots were produced with the ggplot2 package. Mitochondrial DNA sequencing. The COI sequencing was performed in the Canadian Centre for DNA barcoding (CCDB). One leg from each specimen was placed into individual wells in a 96-well plate prefilled with ethanol and the plate was sent to CCDB. There, DNA was extracted and the COI gene (654 bp) was sequenced following standard protocol  82. All collection data and specimens' images were uploaded into BOLD together with successful sequences by corresponding Sample ID (SM1). In addition, publicly available sequences were included. For the full list of samples see Table  S3  (SM1). ddRADseq library preparation.
Raw DNA reads from ddRADseq are available at the NCBI Sequence Read Archive (BioProject ID: PRJNA345307 and PRJNA595572). ddRADseq data bioinformatics. Quality control was performed with FastQC 92. Paired-end reads were assembled de novo using the ipyrad workflow (https ://ipyra d. readt hedoc s. io/). The software provides strict filtering steps to ensure the absence of paralogs and low-quality base calls in the assembly. The most important parameter altered from the default was the minimum number of samples per locus (m). Test datasets were assembled with different combination of the parameter to find optimum values as well as to investigate the influence of missing data on consecutive analyses. Values of m from a minimum of three to a maximum of 22 were tested. Two types of datasets were assembled, the first included all species with an outgroup and the second contained only target species with their closest relatives.
The first dataset was used for phylogenetic inference and p-distance computation and the second set for species delimitation and population genetic analyses. All strict filtering steps allowed for additional control of possible WGA bias. The proportion of missing data was calculated with Mesquite version 3. 51  93. Phylogenetic analyses and haplotype networks. Phylogenetic analyses were required to reveal historical relationships among taxa and to test the validity of the prevailing species hypotheses. Maximum likelihood (ML) trees for ddRADseq datasets were inferred with the IQ-Tree program  94  with 5000 ultrafast bootstrap support for branches  95. Haplotype networks for COI were computed with a modified script in RStudio (origi-.
Summary of the ddRADseq datasets used in the downstream analysis. n: number of individuals; VAR: percentage of variable sites; PIS: percentage of parsimony informative sites. Dataset names are organized as following: Rip_pal-pullata and monticola groups combined plus outgroup species; Rip_pal_IN-same as previous but no outgroup; Rip-pullata group only; Pal_mon-monticola group only; m-minimum taxon coverage; c-clustering threshold.
Acknowledgements We thank  Kyung Min Lee  for help with ddRADseq analysis and comments on the manuscript;  Kirill Mikhailov ,  Sergei Esyunin  and  Galina Azarkina  for unpublished data on distribution of P. riparia and P. palustris;  Sepideh Shafaie  for providing P. pontica specimens;  Mikko Pentinsaari  for help with haplotype networks;  Helsinki University Biological Station (Kilpisjärvi)  and  Tvärminne Zoological Station  for help with spider sampling in Finland;  CSC -IT Center for Science  for providing facilities for computation; staff at the  Biodiversity Institute of Ontario  for their continuous help in generating sequences, entering data into BOLD and aiding the curation of this information; FIMM staff for their high-quality work. Funding was provided by " Suomen Akatemia ", Grant No.
g. a"urban" or"forest" area) are represented by different spectral signatures  (Jensen, 1983) , thus making them difficult to map in pixel-based land cover products using the sole spectral information. Of course, depending on the comprehensiveness of the training dataset, advanced supervised classifiers can deal with such complex classes, e. g. by assigning the"urban" class to different spectral signatures classified in different branches of a decision tree. But, in the case when two leaves of a decision tree have the same label, the interpretability of the classification model turns to be very complex, which tends to widen the semantic gap between the numeric information contained in the pixels and its thematic interpretation.
Inconsistencies between data-driven methods and end-users requirements The second major issue is related to the methodological approach applied to produce land cover maps. Here, we differentiate"data-driven" from"knowledge-driven" methods. "Data-driven" refers to methods that depend on the data that has been collected. In remote sensing, both supervised and unsupervised classifications can be considered as"datadriven" as they depend on the image and on the training (for supervised classifiers) datasets. In other words, if you change the training data or the EO data, you also change the classification model. In opposition,"knowledge-driven" approaches depend on a priori expert knowledge and are thus independent from the data. For example, it is widely agreed by the remote sensing community that high reflectances in the near infrared (NIR) wavelengths are associated to vegetated land cover.
Thus, such algorithms do not match with the way end-users are used to work. As a consequence, users often consider data-driven approaches as"black box" algorithms whose use is not sufficiently documented in land cover mapping methods  (Congalton et al. , 2014). As a partial answer, recent efforts have been carried out to share methods and algorithms (instead of sharing ready-to-use maps) designed for remote sensing-based land surface monitoring (e. g. the CLASLite software  (Asner, 2009) , the EODHaM system  (Lucas et al. , 2015) , the iota2 processing chain  (Inglada et al. , 2017) ). The application of such algorithms benefits from the emergence of online platforms dedicated to the cloud computing of large-scale geospatial data which allow both the production of high resolution maps and the sharing of image processing algorithms  (Gorelick et al.
, 2017). By improving the transparency and democratization of high-performance technologies, such approaches contribute to facilitate the processing of big Earth Observation (EO) datasets by scientists from varied scientific domains, i. e. not only remote sensing experts. However, at the same time it fosters the use of EO data by non remote sensing experts, it also -and rather paradoxically-supports the evolution of remote sensing from an environmental science to a computer science with a major focus on image and signal processing (e. g. improved classification algorithms, analysis of big EO data time series, etc) rather than on the applications. The expert knowledge mobilized by environmental scientists to interpret remote sensing images thus tends to be somehow discarded from the image analysis.
For this reason, we manually discarded unusable data and finally acquired a total of 2069 Level 1-C Sentinel-2 images, i. e. 1793 Sentinel-2A images and 276 Sentinel-2B images. The entire dataset thus consisted in 75 time series including from 13 to 46 images. We used Top-of-Atmosphere reflectances of six spectral bands (R, G, B, NIR, SWIR1, SWIR2) in order to apply the Satellite Image Automatic Mapper (SIAM) algorithm (see Section 3. 2. 1). All bands were resampled at 10 m spatial resolution with a bilinear algorithm in order to benefit from the highest spatial resolution.
Observables: classes of colour-material Since Observables refer to Sense Qualia field-like properties, the major issue consists in assigning meaningful labels (i. e. values) to describe them, especially to describe spectral signatures. In this regard, models of qualitative colour description (e. g. (Falomir et al. , 2013) ) include colour labels (e. g. "pale-blue","light-green") that appear limited to define remotely sensed spectral signatures including reflectances in spectral bands not accessible for human vision. The objective is then to define rules to categorize spectral signatures involving spectral bands from the non-visible spectrum, adding them semantic values corresponding to classes of Colour-Material. We relied on the seminal work from Baraldi et al. (Baraldi et al. , 2006)  to process all images of the database.
For example, the SVHNIR spectral category refers to Strong Vegetation with High Near-Infrared values. All SIAM spectral rules are introduced in  (Baraldi et al. , 2006)  and thus not repeated here. Nonetheless, it is worth noting that, since the method has initially been designed for Landsat-5 (TM) and Landsat-7 (ETM+) images, a few adaptations were required in order to be applied to other optical sensors  (Baraldi et al. , 2010). For example, the absence of the thermal band in Sentinel-2 images needed to be addressed since it is used to discriminate a few spectral categories. In that case, all spectral categories whose corresponding rule set implied the thermal band were removed so that the final Sentinel-2 pre-classifications only considered up to 36 spectral categories whereas the original SIAM version implemented for Landsat data can identify up to 46 spectral categories.
Also, it is worth noting that the red-edge band of Sentinel-2 images was not used in this study since the original version of SIAM designed for Landsat data did not include any rule for this specific band. In addition, the spectral categories corresponding to cloud classes are based on the thermal band and could not be applied to Sentinel-2. Since cloud masking is very important, we created an equivalent spectral category in order to map clouds. For this purpose, we first intended to use the cloud mask product released with Level 1-C Sentinel-2 data. However, this product has been considered as highly inefficient in Amazonian rainforests due to the presence of complex cloudiness and high water vapor content  (Coluzzi et al. , 2018). Various software and algorithms to map clouds (and cloud-shadows) in Sentinel-2 images were then proposed recently  (Frantz et al.
Finally, each pixel time series is described by six scores corresponding to the six classes of colour-material. These scores were then used to derive various information. First, the"Best Possible Score" (BPS) corresponds to the sum of all six scores. It represents the maximum score potentially assigned to a pixel that would always be classified in the same class of colour-material. BPS gives a first broad overview of pixel quality since it is related to the number of observations, thus considering the inhomogeneity of the data  (Sudmanns et al. , 2019). Score BPS = ∑ Score colour-material (2) where Score colour-material refers to the scores of the six classes of colourmaterial. Second, the"Best Land Surface Score" (BLSS) is the sum of all scores corresponding to classes of colour-material related to a land surface material type, i.
Natural vegetation At first, we aimed to assess how efficient the automatic approach was to map areas of natural vegetation. To do so, we relied on the 2008 map of consolidated rural areas (CRA) produced by ICV based on photointerpretation of high resolution images (SPOT 5 images at 2. 5 m spatial resolution) for the entire state of Mato Grosso. We then crossed that map with additional information in order to map remnants of natural vegetation in 2017 (hereafter named NatVeg2017), i. e. at the date of acquisition of the Sentinel-2 images. For this purpose, we used 1) the annual official deforestation maps provided by INPE (Brazilian National Institute for Space Research) through the PRODES program (the Brazilian Deforestation Monitoring Program) and 2) the 2017 MapBiomas land cover map (Collection 5) produced by a multidisciplinary team based on supervised classification of Landsat time series on Google Earth Engine.
In other words, various correct interpretations of a unique dataset of satellite images can coexist, thus illustrating the vagueness issue of geographic terms as mentioned in Section 1. 1. For example, the validation of the natural vegetation land cover class was challenging since our approach did not produce an explicitly defined class of"Natural Vegetation" at the modular stage. Indeed, we rather mapped a gradient of terrestrial vegetation classes. Considering that most tropical native vegetation in the study is evergreen, we focused on the modular classes that were"never" or"rarely" observed as"Brown Mineral" Colour-Material. We ordered them in a gradient ranging from classes corresponding to pixels only classified in"Strong Green Vegetation" (i. e. IDs 811, 711, 611, 511, 411, 311, 211)   IDS 181, 171, 161, 151, 141, 131, 121) , on the other side.
Water bodies With regard to the mapping of water bodies, we used recent (2016) reference land cover maps provided by the end-user for 18 municipalities in northern Mato Grosso (hereafter named LC2016_18). These maps are similar to LC2016_4 except they do not contain information on the delineation of PPA and thus cannot be used to validate riparian forests. But, LC2016_18 maps contain many information on the location of natural and artificial water bodies and can thus be used to assess the potential of the approach to map this class. For this reason, we compared the"Water" samples from the reference data with their corresponding pixel values produced at the dichotomous stage, either considering only the"Water" class or both classes of"Water" and"Aquatic Vegetation". In addition, since the width of water courses was informed by the end-users in the database, Fig.