dataesr/scientific-paragraphs-categorization huggingface dataset:
Imagery datasets and processing Mars surface elevation is described by various elevation datasets. First, a global model was produced by Mars Orbital Laser Altimeter (MOLA) onboard Mars Global Surveyor (MGS). Elevations from MOLA have been converted into a global DEM with a spatial resolution (Rs) of 463 m/pixel  [38]. Other Martian DEMs are produced by stereo-photogrammetry, from two images taken from different observation angles, and are aligned on MOLA data. Mainly three instruments are used to perform stereo-photogrammetry on Mars. The High Resolution Stereo Camera (HRSC) onboard Mars Express (Rs = 12-25 m/pixel)  [39, 40] , CTX onboard MRO (Rs = 6 m/pixel)  [41, 42]  and HiRISE (High Resolution Imaging Science Experiment) also onboard MRO mission (Rs = 50 or 25 cm/pixel)  [43, 44]. The increasing coverage of Mars by visible imagery allows creating DEMs by stereophotogrammetry with a better spatial resolution than MOLA.
However, computed products may contain more noise than a manually calibrated DEM. In order to quantify the coverages, we computed global coverages of the various data-set using MarsSI footprints. Despite being the highest spatial resolution, HiRISE data suffers very low spatial coverage (~4% of image coverage and ~0. 3% of stereo-coverage). CTX images offer a very good trade-off between coverage and resolution with a resolution allowing to assess the morphometry of craters down to 50 m of diameter and a coverage large enough to provide sufficient crater statistics (almost 100% of image coverage and~17% of stereo-coverage). To better explore the ever larger CTX dataset, we propose a semi-automated depth measurements workflow, with a manual crater mapping a CTX image and an automatic rim to floor depth extraction from an aligned DEM.
Description of the automatic pipeline We provide aside this paper a code written in python. From a craters map saved as a shapefile and an associated DEM in the same projection system, this program computes depth for each crater and adds it, as a new attribute, to the shapefile. This section describes the algorithm used to measure depth. The elevation of the floor of the crater can simply be estimated by the minimum elevation within the crater. However, DEMs may contain artifacts both of natural origin (subsequent craters on the floor) or artificial origin (DEM computation errors). To better assess the deepest point of the initial crater, we choose to use a low percentile. For each crater, we extract the elevation values of each pixel within the circle. Those elevation values are weighted by the area of the surface of the pixel included within the circle.
Creation of the test dataset In order to design and calibrate our automatic measurements and assess the precision of our depth measurements, we performed manual crater rim to floor depth measurements using HiRISE DTMs and orthoimages, as this data-set has a higher spatial resolution than CTX data set. Our calibration set is composed of 182 craters depth measurements at HiRISE scale where HiRISE and CTX DEM were overlapping. Table  1  lists the used images. Fig. 2  is a histogram of diameters and depth of the test sample, the size frequency distribution follows a power law, which is representative of a classic crater database  [49]. Crater depth can be either defined as the difference between the local surface and the bottom of the crater elevations or as the difference between the rim and the crater bottom elevations (  [33]  and references therein).
Parameter tests For a crater i, we note d a,i , the depth measured by our algorithm on CTX dataset and d m,i , the precise data measured on HiRISE (we consider the error on this measurement to be negligible). We note r i = d a,i -d m,i the residual error and R the set of the 182 residual errors. We computed the sum of the absolute values of residual error x ¼ P i r i j j to test the different percentile values. The lowest sum of residual error will determine the best percentile to use. We computed x using different estimations of floor elevation and rim elevation. Fig. 4  represents the result of this computation for various combination of tested rim and floor elevations. As shown in Fig. 4 , the floor elevation estimated as the 3 rd percentile of elevation value within the crater, and the rim elevation as the 60 th percentile of elevation values on the circle edge are the measurements minimizing the error.
We set these parameters of our python code, but they can be easily tuned. Fig. 4  shows that simply taking the median of elevation around the crater and the minimum elevation within the crater results in large residual error. Especially, crater floor elevation should not be approximated by the minimal elevation within the crater. Using a low percentile value provide better results for a slightly higher computation time. The minimum elevation is probably too sensitive to DEM artifacts and local elevation anomalies like smaller craters on the floor. Fig. 5  shows a  histogram of the residual error. This histogram can be described by a Laplacian law with a standard deviation of 5 m. This standard deviation seems independent from diameter or depth.
Interpretation of measured depths This method allows the extraction of an extended crater morphometry dataset. However, the error on the measure is quite important for shallow craters. Considering a maximum depth over diameter ratio of 0. 2 (  [33]  and therein), most craters less than 100 m of diameter are expected to have a depth less than 5 m. We recommend not to use this pipeline to measure depth for crater smaller than 100 m on CTX image. We also recommend not to use depth measured with this algorithm to interpret single crater depth as the error from a single crater depth measure may be large while on a wider data-set, the results can be statistically significant. This algorithm is, indeed, designed to compute depth statistics for a large number of craters, typically a crater set of several hundred craters like the ones used for crater-based chronology.
We provide a python code that reproduce our pipeline. With this program and the MarsSI application, it becomes easy to compute large dataset of crater depth. This method may be extended to other datasets, as long as a crater shapefile is associated with a DEM. In this case, supplementary work should be done to assess errors on the measurements. With about 17% of global coverage by CTX DEM, the algorithm offers large perspective to widely analyze the depth distribution of Martian craters down to 100 m. Such large depth dataset should help to better understand to interaction between surfaces processes and cratering mechanisms based on their shape distribution.
Thus, the Z-axis data are only related to signal intensity (greyscale) giving no metrological information. Consequently, NP dimensions extracted from SEM images contain only the dimensions in the XY-plane. Generally, the measurand used for the described NP size in SEM is an areaequivalent diameter. This corresponds to the diameter of a sphere with the same projected surface as the studied nanoobject. However, from this imaged NP surface area, other measurands such as minimum and maximum Feret diameters can be defined as well  [5]. As a result, SEM and AFM are complementary. Indeed, SEM gives no quantitative information about the NP height, whereas the uncertainty associated with the AFM measurement of the NP maximum point is close to 1. 5 nm  [1]. Conversely, the lateral dimensions measured by AFM are impacted by tip/NP convolution, whereas latest-generation scanning electron microscopes equipped with field-emission guns can reach a resolution of 1 nm in the XY-plane.
Thus, hybrid metrology consists in using several measuring techniques and their associated metrologies to combine their strengths and limit their weaknesses. Combined with statistical tools and input data, such as chemical information and crystal structure, hybrid metrology makes it possible, after data fusion, to obtain more reliable measurements and uncertainties better than the uncertainties associated with the measurements provided by each instrument  [10]. A previous study had been performed on the comparison of AFM and SEM measurements on the same set of nanoparticles  [11]. This study especially proposed a robust Matlab routine for data processing from AFM and SEM measurements on nanoparticles. The comparison between both techniques had been carried out on a single silica NP population with few NPs measured.
Basic measuring principle and data processing In this paper, the 3D characteristic dimensions of a NP silica population are defined through the NP height measured by AFM and their lateral diameters (min and max Feret diameters) measured by SEM. The spherical silica NP height, H AFM , was determined by subtracting the NP maximum point Z-coordinate measured by AFM from the position of the mean roughness plane as considered in  [1]. The measuring principle is schematized in Figure  1. The roughness of the substrate surface impacts the uncertainty associated with the height measurement  [1]. A silicon wafer has been chosen as substrate for NP deposition because its roughness is relatively low (S q = 0. 3 nm), its surface physicochemical features are particularly suitable for an optimized NP dispersion and its electrical properties are compatible with SEM measurements  [11].
The selection of species will accommodate the research needs of all WPs, which may focus on subsets of the species list, depending on the WP's objectives, data and resources availability. Priority will be given to species already included or expected to be included in the "List of IAS of EU concern", a list of 49 (+18 candidates) plants and animals whose management has been prioritised by the European Union under Regulation 1143/2014. In the absence of similar official species listings in the US, we will first collate information about the impacts of invasive species on biodiversity and ecosystem services with EICAT and then select 100 high-risk plants and marine organisms. Intervention scenarios will use as reference the Alien Species Narratives (ASNs), developed by AlienScenarios (https://alien-scenarios.
This information will then be combined with species distribution models (SDM) under current and future climate conditions in Europe and the US (WP3-4). Local-scale studies combining field data and scenario generation (through InVEST) will be used to evaluate the real-world costs and benefits of IAS management (WP5). Deliverables of the project include spatially-explicit assessments of the threat posed by invasive species to biodiversity and ecosystem services under a range of climate-change and intervention scenarios. ing globalisation. Under a best-case scenario, international co-ordinated actions prevent the arrival of new invaders -through, for instance, border control and trade regulations -and existing invasive species are managed via eradication techniques and ecological restoration.
WP 4. Assessing and mapping the impacts of invasive species on biodiversity and ecosystem services in the US In this WP, we will identify range-shifting invasive plants that have not yet been reported in parts of the US but are projected to expand with climate change. This will help anticipating the threat posed by invasive species to biodiversity and ecosystem services under alternative intervention scenarios. We will first develop a database synthesising all ecological, agricultural, economic and human health impacts reported in the scientific literature and use S/EICAT protocols to evaluate impacts. Then we will capitalise on available occurrence data to model the potential distribution of ca. 100 range-shifting invasive plants. This approach is focused on emergent threats, whereas established species with the potential to become invasive under climate change, often called sleeper species  (Crooks 2005) , are out of the scope of this project.
Finally, we will use the software InVEST (https://naturalcapitalproject. stanford. edu/ invest/) to spatially model the provision of ecosystem services, biodiversity and tradeoffs at the local/regional scale  (Nelson et al. 2009 ). All species sampled in WP5 will be modelled in WP3-4, evaluated with S/EICAT (WP2) and considered in the elaboration of management scenarios (WP1). In contrast to the continental evaluations of WPs 3 and 4, case studies will provide real on-the-ground information to test the accuracy of impact assessments like S/EICAT and to quantify the real costs and benefits of alternative management decisions. While case studies share a common work plan for the evaluation of impacts, the specific field methodologies differ depending on the habitat, invasive species and ecosystem services targeted in each case.
The impacts of the blue crab may be more difficult to assess because it is a highly mobile and territorial species. For this reason, we will consider using enclosures to further investigate changes in ecosystem services caused by the blue crab. With this information, we will finally employ the software InVEST to explore spatially how alternative intervention scenarios may affect ecosystem services at the local scale. Case study 2-Terrestrial ecosystems. We will focus on two terrestrial plants that are highly invasive in semi-natural meadows and forest edges across Europe: the Canadian goldenrod (Solidago canadensis L. ) and the Himalayan Balsam (Impatiens glandulifera Royle). The impacts on biodiversity and ecosystem services of these two invasive plants will be studied in detail in two nature reserves located in the French Alps lowlands: the "Reserve du Bout du Lac d'Annecy" and the "Marais de Giez".
Figure1. InvasiBES aims to use scenarios and models to understand and manage the impacts of Invasive Alien Species on biodiversity and ecosystem services. First, we will develop intervention scenarios with other scientists and stakeholders and select Invasive Species of Interest for the project (WP 1). Second, we will use standard protocols such as EICAT and SEICAT (S/EICAT) to evaluate the impacts of species on biodiversity and ecosystem services (WP 2). This information will then be combined with species distribution models (SDM) under current and future climate conditions in Europe and the US (WP3-4). Local-scale studies combining field data and scenario generation (through InVEST) will be used to evaluate the real-world costs and benefits of IAS management (WP5). Deliverables of the project include spatially-explicit assessments of the threat posed by invasive species to biodiversity and ecosystem services under a range of climate-change and intervention scenarios.
Acknowledgements InvasiBES was funded through the  2017-2018 Belmont Forum and BiodivERsA joint call for research proposals , under the  BiodivScen ERA-Net COFUND  programme and with the following funding organisations: the  Spanish Ministry of Science, Innovation and Universities  ( PCI2018-092986  and  PCI2018-092939 ,  MCIU/AEI/FEDER, UE) , the  Swiss National Science Foundation  ( 31BD30_184114  and  31003A_179491 ), the  US National Science Foundation  ( ICER-1852326 ), the  German Federal Ministry of Education and Research BMBF  ( 01LC1803A ), and the  French National Research Agency  ( ANR-18-EBI4-0001-06 ). BG is currently supported by the  Biosecurity Initiative at St. Catherine's, BioRISC  (http://www. biorisc. com)and JMJ is additionally supported by the  Deutsche Forschungsgemeinschaft DFG  ( JE 288/9-2 ).
2016 ) and found thousands of such variables. We add to this census by presenting new RRL candidates discovered in the first three years of the DES data. DES is a five-year multiband (grizY ) imaging survey using the Dark Energy Camera  (Flaugher et al. 2015)  on the 4-m Blanco Telescope at Cerro Tololo Inter-American Observatory (CTIO). After the conclusion of its observations, DES will provide a deep (∼ 25 mag in the final coadded images) and wide (∼ 5000 deg 2 ) dataset near the Southern Galactic cap  (DES Collaboration 2005; Diehl et al. 2016). By the end of the survey, the entire footprint will have been imaged ∼ 10 times in each band. While the main goal of the survey is to better constrain certain cosmological parameters, the deep and wide survey data provide an excellent test bed for probing Milky Way substructure with RRL.
DES Year 3 Quick Release This work is based on the DES internal Year 3 Quick Release catalog (hereafter Y3Q2), which contains all the single epoch data from years 1-3 that formed the basis for the coadded DES first public data release 4  (DES Collaboration 2018, hereafter DR1). The Y3Q2 data set was developed in the same manner as the Y2Q1 data release used for the stellar overdensity searches in  Drlica-Wagner et al. (2015)  (see their §2. 1 for a detailed description of how the Quick Release catalogs were generated), with one major change. Instead of using stellar locus regression (SLR  Ivezić et al. 2004; MacDonald et al. 2004; High et al. 2009; Gilbank et al. 2011; Desai et al. 2012; Coupon et al. 2012; Kelly et al. 2014)  to determine zeropoints for the absolute photometric calibration, the Y3Q2 release utilizes the Forward Global Calibration Module (FGCM) photometric zeropoints  (Burke et al.
3  in  Diehl et al. 2016). While this strategy ensures uniform depth and the best use of the observing time, objects in the wide-field survey are sampled with a highly unpredictable cadence. In the Y3Q2 data set, individual objects can have from 2 to over 50 observations depending on their location. We ensure that each light curve only contains photometric observations by requiring that each observation has a SExtractor warning value FLAG ≤ 4, is sufficiently far away from masked regions in the images (IMAFLAG ISO ≤ 4), and has a zeropoint correction available (FGCM FLAG ≤ 4). After these cuts, the median number of total observations for a given object is 10, while the median number of observations in each band across the survey region is 4. The effects of the survey coverage and these cuts are discussed more in §6.
Although RRL have well-characterized colors, we did not employ a color cut in this early stage of the analysis because we did not want to exclude any potential RRL with poor coverage across filters or pulsation phase. Simultaneous colors were not available for some objects in the DES footprint, so we would have to calculate colors using coadded magnitudes or magnitudes from arbitrary phases in the star's variation to calculate colors, which would expand the range of possible colors for RRL in our data. The RRL template we describe in §4. 1 provides the color information we need to identify RRL candidates. In the future, when more epochs of DES data are available, color cuts will be a more reliable RRL indicator prior to the template fitting. In summary, our combined selection criteria were: • ≥ 2 observations in g,r,i, or z • |spread model| ≤ (0.
(2007) were still selected. It is possible that some of these objects are truly variable sources that did not display significant changes in the previous studies. Another possibility is erroneous photometry that, while rare, occurs sometimes in the Y3Q2 dataset due to incorrectly attributing observations from separate sources to one object or imperfect masking of observations obtained in very poor weather conditions. While these objects may have passed the initial variability cuts, their light curves fit the RRL template poorly, and most of them were rejected later in our analysis. All of the selected light curves were corrected for extinction using reddening values from the maps of  Schlegel, Finkbeiner & Davis (1998)  multiplied by filter coefficients derived from the Fitzpatrick (1999) reddening law (for R V = 3.
4 and Table  4. The computation time for fitting the template and calculating features for ∼ 7 × 10 5 light curves was ∼ 44K CPU hours. For comparison to a similar analysis, our algorithm is > 9× faster than the template fitting methods used by  Sesar et al. (2017) , which required ∼ 30 minutes per star. There are several other well-documented methods available in the literature for identifying RRL in multiband data (e. g. VanderPlas & Ivezić 2015 , Hernitschek et al. 2016 and Sesar et al. 2017) , which yield excellent results for data sets with an average of 35 or more observations per light curve. We present this algorithm as an alternative to these other methods for especially sparse multiband data sets (see §5. 3 for a discussion of the observational limitations. ) The template and the associated fitting code are available at https://github.
Once trained, the random forest algorithm assigns a score to unlabeled data based on the proportion of trees that identify them as a particular class. Random forest classifiers have been extremely successful in variable star classification (see  Richards et al. 2011  for a comparison with other machine learning techniques), even in the case of sparsely sampled Pan-STARRS PS1 light curves  (Hernitschek et al. 2016 ). Thus, the random forest was a natural choice of classifier for this study. We created the classifier using the RandomForest package available in scikit-learn  (Pedegrosa et al. 2011). To prevent overfitting to our small training set and ensure repeatability, the classifier contained 500 trees with a maximum depth of 5, and used a random seed of 10. We assessed the performance of our classifier by estimating the purity (the percentage of objects classified as RRab that were truly so) and the completeness (the percentage of real RRab that were identified as such) as a function of the class score reported by the random forest.
Visual Validation We applied the classifier to the ∼ 7 × 10 5 objects with template fits and found 8026 RRL candidates with a score ≥ 0. 35. Although most of our candidates were indeed RRL found in other surveys, there were still some non-stellar interlopers in the sample due to the lenient initial cuts on the shape of the photometric point-spread function ( §2. 2). Thus, we visually inspected all RRab candidate light curves and their DR1 coadded images. After visually validating the candidates and removing any objects with non-RRL classifications in the Simbad database  (Wenger et al. 2000) , 1786 objects were discarded and 5783 RRL candidates remained in the catalog, with the rest too ambiguous to confirm. A sample of visually verified candidates with high (p > 0. 94) and low (p < 0. 36) classifier scores are shown in Figures 9 and 10, respectively.
Comparison with Overlapping Catalogs The DES footprint has significant overlap with other surveys, such as Gaia (Gaia Collaboration et al. 2016), Pan-STARRS  (Chambers et al. 2016) , the Catalina Surveys  (Drake et al. 2009) , and the Asteroid Terrestrialimpact Last Alert System (ATLAS:  Tonry et al. 2018). We used our cross-matches with these external RRab catalogs to independently assess the performance of our algorithm at the different magnitude ranges probed by these surveys. We used the SkyCoord package in astropy (Astropy Collaboration 2018) to select matches within 1 of DES objects while removing duplicates. Details for each individual survey are presented in the following paragraphs and summarized in Table  7 , while Figure  13  shows the respective overlaps with DES. Clementini et al.
2 correspond to the LMC outskirts and the Fornax dSph. Our catalog is deeper than the others by ∼ 1, 1, 2 and 4. 5 mag, respectively. In addition to searching for RRab candidates with previous identifications from the aforementioned wide-field surveys, we also checked for overlaps near the Magellanic Clouds  (Soszyński et al. 2016) , the Fornax dSph  (Bersier & Wood 2002) , the Sculptor dSph  (Martínez-Vázquez et al. 2016) , in the General Catalogue of Variable Stars  (Samus' et al. 2017) , and in the SIMBAD database  (Wenger et al. 2000). To the best of our knowledge, and based on publicly available catalogs, 1795 (nearly 31% of our sample) are newly-discovered RRab candidates. Although the external catalogs under consideration are not complete, the fraction of their RRab recovered by our analysis is consistent with our estimate of ∼ 75% completeness.