dataesr/scientific-paragraphs-categorization huggingface dataset:
These candidate variables can be further explored by using m w cell values in post-MAPI analyses. To illustrate this possibility we used a regression model accounting for spatial autocorrelation in m w cell values and fitted in a Bayesian framework. From our simulated datasets, the regression model successfully retrieved the expected relationships (i. e. smaller m w values within the favourable habitat) for the fragmented and gradient landscapes but not when the two habitats This article is protected by copyright. All rights reserved. were randomly distributed. These results can be explained by the difference in the scale at which habitats are spatially autocorrelated in the three simulated landscapes. As long distance effects are limited in the computation of m w by using an inverse-area-weighting procedure, the cells mainly reflect the average intensity of the shorter connections crossing at one location.
Consequently, significant relationships between m w cell values and landscape variables are expected to be detectable when the landscape is spatially structured in such a way that a cell is mostly influenced by short-distance connections occurring between samples located within a relatively homogeneous landscape aggregate. Contrarily, when the spatial scale of autocorrelation in landscape features is smaller than the resolution of the sampling, a cell reflects between-as much as within-habitat connections and habitat effect are likely to become undetectable (e. g. random landscape). In the datasets simulated under a fragmented landscape model, sampling size and strategy did not affect the detection of habitat effect. Under the gradient configuration, the habitat effect was well detected especially when a large number (i.
In such a situation, increasing the neighbouring structure from two to five cells in the conditional autoregressive model significantly improves the detection of the habitat effect from the balanced sampling (i. e. mean posterior probability = 0. 909 and 0. 927; percentage of significant probability = For each simulated dataset and sampling scheme, we extracted cell-specific m w values and habitat type (1 or 2) to fit the following regression model: =∝ + , where ∝ is an intercept depending on the habitat type in cell i and error term ε i includes a structured and an unstructured component. Here are presented the posterior probability that ∝ <∝ (P(α 1 < α 2 )) averaged over 20 replicates ( ) as well as the proportion of P(α 1 < α 2 ) ≥ 0. 95 (P 95% ) and ≤ 0. 05 (P 5% ). Note that for all simulated test datasets, the regression model was well adjusted with a mean predictive p-value very close or equal to 0.
, to test whether the pairwise metric values associated to the ellipses are independent of the sample locations (i. e. under the null hypothesis that pairwise metric values, and then m w cell values, are randomly distributed in space). Sample locations from the observed dataset (Figure2A1) are permuted n p times (Figure2B1) (parameter n_permutations). At each permutation, new m w values are computed and stored to build a cumulative null distribution for each of the n c cells of the grid (Figure2B2). Each m w cell value from the observed dataset is then ranked against its null distribution (Figure2C1). For each cell, the proportion of permuted values that are smaller than the observed value provides a lower-tailed test (LT) p-value. An upper-tailed Comment citer ce document : Piry, S. (Auteur de correspondance), Chapuis, M.
2 and 2. 3 of the Appendix. For each of the simulated datasets, we also investigated MAPI sensitivity to sampling effort by sampling 200 and 500 individuals under three sampling schemes : (1) random sampling, in which individuals were sampled anywhere in the landscape regardless to the habitat; (2) balanced sampling, in which an equal number of individuals was sampled from each habitat; (3) gridded sampling, in which the same.
Parameters for each spatial model are indicated, including: the product of the effective population size and migration rate per generation (N e m); the shape parameter of the geometric distribution (g); the number of simulated populations (Ksim); and the number of loci (Nloc). Performance is measured as the average percentage, over 25 replicates, of the barrier covered by significant discontinuous areas (Coverage), and the average percentage, over 25 replicates, of significant discontinuous areas having no contact with the barrier (Unexpected). Large sampling refers to the sampling of 200 genotypes distributed regularly on the lattice, and small sampling to the sampling of 75 genotypes drawn randomly from the lattice. The datasets with 100 loci are indicated in italics. Comment citer ce document : Piry, S.
ACKNOWLEDGEMENTS The authors thank  T. Jombart ,  G. Guillot ,  N. Verzelen ,  R Leblois  and  A. Estoup  for helpful comments on previous versions of the manuscript as well as the associate editor and four referees for their very constructive remarks. The authors also thank  C. Desbiez  and  C. Brouat  for providing the virus and forest ground beetle data, respectively. K. Berthier  acknowledges the  INRA Metaprogram "Sustainable Management of Crop Health"  for funding the  COPACABANA  project. This work is dedicated to late Serge Meusnier.
DATA ACCESSIBILITY -Rodent microsatellite data: DRYAD entry doi. org/10. 5061/dryad. jf7sn, -Plant virus sequence data: Genebank accession numbers EU660581-EU660590, HM044202-HM044215, -Ground beetle microsatellite data: Figshare entry https://figshare. com/articles/GroundBeetles_zip/3482804 and French National Institute for Agricultural Research, https://www1. montpellier. inra. fr/CBGP/software/MAPI/ (file: MAPI_examples. zip). -Simulated test datasets and R scripts: French National Institute for Agricultural Research, https://www1. montpellier. inra. fr/CBGP/software/MAPI/ (file: MAPI_examples. zip).
Simulation To analyzing the performance of the previously mentioned algorithms, we realised several simulations using MATLAB software. These simulations allowed to simulate the exploration process in two different environments depicted in figure  1  which represent offices (Fig. 1. a) and randomly generated mazes (Fig. 1. b ). This simulated environments are discretised into occupancy grids. Each cell of this grid is considered as a unit with resolution of 0. 5mx0. 5m and all the simulated environments previously described have a size of 25mx20m which represents 50 x 40 units. Furthermore, we assume that all robots are identical and have the same size as the cell of the grid, so, each of them represents one unit in the map. We also assume that their location is known with certainty and all of them can scan their neighbourhood at 360° around them inside a parameterised sensor radius which is fixed in our experimental simulation to 4 units.
After the selection of Gaia in 2000 as an ESA-only mission, followed by further preparatory studies, the implementation phase started in 2006 with the selection of the prime contractor, EADS Astrium (later renamed Airbus Defence and Space), which was responsible for the development and implementation of the spacecraft and payload. Meanwhile, the complex processing and analysis of the mission data was entrusted to the Data Processing and Analysis Consortium (DPAC), a pan-European, nationally funded collaboration of several hundred astronomers and software specialists. Gaia was launched in December 2013 and the five-year nominal science operations phase started in the summer of 2014, after a half-year period of commissioning and performance verification. Unlike the Hipparcos mission, the Gaia collaboration does not have data rights.
As for the photometers, however, the data quality will be severely compromised in dense areas by contamination from and blending with nearby sources. Gaia Collaboration et al. : The Gaia mission 3. 3. 8. Video processing unit and algorithms Each CCD row in the focal plane (Sect. 3. 3. 2) is connected to its own video processing unit (VPU), essentially a computer in charge of commanding the CCDs and collecting the science data and transmitting it to the onboard storage (Sect. 3. 3. 9). The VPUs run the video processing algorithms (VPAs;  Provost et al. 2007) , which are a collection of software routines configurable through a set of parameters that can be changed by telecommand. The seven VPUs are fully independent although each one runs the same set of VPAs albeit (possibly) with different parameter sets.
Science operations The science operations of Gaia are conducted by the Science Operations Centre (SOC) located at the European Space Astronomy Centre (ESAC) in Villafranca del Castillo, Spain. The Gaia SOC is an integral part of the Gaia DPAC (Sect. 7) and therefore has, in addition to the classical roles outlined in this section, a number of other responsibilities towards DPAC. These other responsibilities are forming the main hub among the six data processing centres (DPCs; Sect. 7. 4) of DPAC; providing system architecture and support functions for DPAC and hosting the main database (MDB) of the mission; operating the daily pipeline (Sect. 7. 1) and disseminating all data products to other DPCs (hub-and-spokes topology); operating and co-developing the astrometric global iterative solution (AGIS; Sect.
Interface to the Mission Operations Centre The SOC is the prime interface to the MOC for all payload-and science-related matters. Normal work includes generating the scanning law (Sect. 5. 2), including the associated calibration of the representation of the azimuth of the Sun in the scanning reference system in the VPU software; generating the science schedule, i. e. the predicted onboard data rate according to the operational scanning law and a sky model, to allow for adaptive ground-station scheduling (Sect. 5. 3. 1); generating the avoidance file containing time periods when interruptions to science collection would prove particularly detrimental to the final mission products; generating payload operation requests (PORs;  Mora et al. 2014a) , i. e. VPU-parameter updates (e. g. TDI-gating scheme or CCD-defect updates); tracking the status and history of payload configuration parameters in the configuration database (CDB) through the mission timeline and telecommand history; hosting the science-telemetry archive; generating event anomaly reports (EARs) to inform downstream processing systems of bad time intervals, outages in the science data, or any (onboard) events that may have an impact on the data processing and/or calibration; monitoring (and recalibrating as needed) the star-packetcompression performance; monitoring (and recalibrating as needed) the BAM laser beam waist location inside the windows; reformatting the optical observations of Gaia received from GBOT for MOC processing in the orbit reconstruction (Sect.
Daily processing As soon as science telemetry arrives from MOC at SOC, its processing in the daily pipeline is initiated  (Siddiqui et al. 2014 ). After the MOC interface task (MIT), which provides a first-level data reconstruction into source-packet groups, and the decompression and calibration services (DCS), which decompresses, identifies, and stores the various data packets in a relational database, the initial data treatment (IDT; Fabricius et al. 2016 ) is run. The IDT processing includes reconstructing all details for each observation window, for example location, shape, and TDI gating, and calculating image parameters (flux and alongscan location, i. e. observation time) and preliminary colours. Also, IDT constructs a coarse version of the spacecraft attitude. This on-ground attitude version 1 (OGA-1) has sufficient accuracy that the observed sources are either identified in the initial Gaia source list  (Smart & Nicastro 2014, IGSL)  or added as new sources during the cross-matching.
The IDT data, which has positional accuracies below 0. 1, forms the basis of both the asteroid and photometric science alert pipelines  (Tanga et al. 2016; Wyrzykowski 2016). The IDT data is disseminated to the data processing centres in DPAC (Sect. 7) for further downstream processing through the main database (MDB;  Siddiqui et al. 2014 ).
The remainder of this section provides a summary description of the Gaia data processing, which serves as the context for more detailed expositions of how specific parts of a Gaia data release are derived from the raw observations. The Gaia data processing, which is summarised below, is a very complex task, serving a wide variety of scientific goals. The data processing can be split into two broad categories: daily and cyclic. The daily tasks produce the preprocessed data that are needed by the cyclic systems, provide payload health monitoring, and feed the alerts systems. The daily systems process the Gaia telemetry in near-real time as it comes down from the spacecraft. In contrast, the cyclic processing iterates between calibration and the determination of source parameters (to be interpreted here in a broad sense, ranging from astrometry to astrophysical characteristics), by repeatedly processing all the Gaia data until the system converges.
Daily data processing The following daily tasks run within the DPAC: Initial Data Treatment and First Look. Following the reception and reconstruction of the telemetry stream at the SOC (see Sect. 6. 2), the IDT and First-Look systems together take care of the preprocessing of the incoming raw Gaia observations (so that they can be treated in the subsequent cyclic processing) and the daily payload health monitoring. These systems run at the DPAC data processing centre hosted at the Gaia SOC and are briefly described in Sects. 6. 2 and 6. 3 above, while the detailed description is provided by  Fabricius et al. (2016). Astrometric Verification Unit. Two systems that independently treat the raw data served by IDT are run as part of the astrometric verification unit, which aims to independently verify all the critical components contributing to the Gaia astrometric error budget.
For further information on the RVS processing see  Katz et al. (2011). Science alerts. Because Gaia repeatedly scans the entire sky and quasi-simultaneously measures the position (to a spatial resolution of 50-100 mas), apparent brightness, and spectral energy distribution of sources, it forms a unique transient survey machine, for example capable of discovering many thousands of supernovae over the course of its lifetime (e. g. Hodgkin et al. 2013). Therefore, DPAC runs a system that uses the IDT outputs, including source positions (already accurate to better than 100 mas) and fluxes, to build up a history of the observed sky to enable the discovery of transient phenomena, for which spectro-photometry is immediately available at each epoch. The candidate transients are then filtered and the most interesting candidates are published as alerts, including the relevant Gaia data to enable rapid follow-up with ground-based telescopes.
Cyclic data processing The self-calibrating nature of Gaia (Sect. 3. 1) is reflected in the iterative processing of the data, which aims to derive both the source parameters and the calibration (or nuisance) parameters that together best explain the raw observations. This iterative or cyclic processing is bootstrapped by the initial data treatment in the daily systems (Sect. 7. 1) and subsequently proceeds by iterating the following steps: 1. Update the basic calibrations, such as the point spread function (PSF) model (including detector charge-transferinefficiency effects), wavelength calibrations, the CCD-PEM non-uniformity calibration, the straylight and background model, etc. 2. Reprocess all the raw observations using the latest calibrations. This step includes the improvement of the matching of Gaia observations to sources (which includes the creation of new sources where necessary), that is the cross-match.
8. 3; David et al. 2014). For the brightest subset, epoch spectra and epoch radial velocities will be available. The iterations between calibrations and spectra and source parameters is performed entirely within the RVS pipeline  (Katz et al. 2011). The above processing steps provide the basic data needed for further analysis and improvement of the astrometric, photometric, and spectroscopic results from Gaia. For example, AGIS treats all sources as point sources and will thus produce suboptimal astrometric solutions for astrometric binaries. Likewise, the alignment of the Gaia reference frame to the ICRF relies on a high-quality selection of QSOs from the Gaia data itself, which will include many previously unknown QSOs from poorly surveyed areas of the sky. AGIS itself does not have the means to decide whether or not a source is a QSO.
Such a selection requires further analysis of the Gaia astrometry and photometry combined. The DPAC therefore also carries out a number of data analysis tasks that, on the one hand, provide higher level scientific data products (such as source astrophysical parameters or variable star characterisation) and, on the other hand, serve to refine the astrometric, photometric, and spectroscopic processing (e. g. by properly treating binaries or providing a clean selection of QSOs for reference-frame alignment). The following cyclic processing tasks provide this higher level data analysis: Non-single-star (NSS) treatment This pipeline  (Pourbaix 2011)  provides a sophisticated treatment of all binary or multiple sources, including exoplanets. The astrometric data for any source that is found not to conform to the single-star source model will be treated with source models of increasing complexity, varying from the addition of a perspectiveacceleration term to the derivation of orbital parameters for astrometric binaries.
(2016)  and  Clementini et al. (2016). Astrophysical parameter inference This system analyses the combination of astrometry, photometry, and spectroscopy from Gaia to derive discrete source classifications and infer the astrophysical properties of the sources. The source classification distinguishes single stars, white dwarfs, binaries, quasars, and galaxies, with the quasar selection feeding back into the astrometric processing as described above. The astrophysical parameters derived from the BP/RP data for stars are the effective temperature, surface gravity, metallicity, and extinction. From the RVS spectra of the brightest stars, αelement enhancements and individual elemental abundances can additionally be derived. Descriptions of the status of this system can be found in  Bailer-Jones et al.
(2013)  and  Recio-Blanco et al. (2016). All the results from the analysis tasks above will also be published as part of the Gaia data releases, thus complementing the basic astrometry, photometry, and spectroscopy to provide a rich data set, ready for exploitation by the astronomical community. Moreover, these analysis tasks feed back into the preceding processing steps and thus provide an essential and strong internal quality control of the DPAC results. The analysis tasks above will not necessarily lead to results that are mutually consistent or consistent with the preceding processing steps. This necessitates an additional, complex task within DPAC, called catalogue integration, which is in charge of integrating, at the end of each iterative cycle, all the results from the various processing chains into a consistent list of sources and their observational and astrophysical parameters.
This list then forms the basis for a next cycle of iterative processing and also for the public Gaia data releases. In order to achieve the iterative improvement of the Gaia processing results, within a given processing cycle all the data collected from the start of the mission are processed again to derive the basic astrometric, photometric, and spectroscopic data (thus involving the upstream systems IDU, AGIS, AVU-GSR, photometric, and RVS processing). To keep this process manageable, the data collected by Gaia are split into data segments, where each segment covers a certain time range. During a given data processing cycle n, typically all the data segments treated during cycle n -1 plus the latest complete segment are processed again by the upstream systems. During the same cycle n, the downstream systems (NSS, SSO, SEA, EO, variable star analysis, and astrophysical-parameter inference) will process the astrometric, photometric, and spectroscopic data derived from the data segments used during cycle n -1.
Simulations, supplementary data and observations, data publication Apart from the processing tasks mentioned in the previous sections, the DPAC responsibilities also include supporting tasks that are necessary for the successful preparation and execution of the Gaia data processing and the publication of its results: Simulations. Data simulations formed an essential element of the DPAC preparations for the operational lifetime of both Gaia and the data processing. The simulations spanned the three levels from the pixels in the focal plane (GIBIS;  Babusiaux et al. 2011) , through simulated telemetry (GASS;  Masana et al. 2008)  to simulated DPAC data products (GOG;  Antiche et al. 2014) , which were used both internally and to support the astronomical community in preparing itself for the Gaia mission  (Robin et al.
2012; Luri et al. 2014 ). These last simulations have been made available and will continue to be available alongside the released Gaia data. Relativistic astrometric models. The proper modelling and interpretation of astrometric data at the micro-arcsecond level accuracy aimed for by the Gaia mission requires the data processing to be fully compatible with general relativity (for example to accurately account for the effect of light bending by solar system objects on the apparent source directions). The relativistic model used for AGIS is described in  Klioner (2003 Klioner ( , 2004)) , while the model employed for AVU-GSR is documented in  de Felice et al. (2004 de Felice et al. ( , 2006)). The relativistic modelling of the observations also requires an up-to-date solar system ephemeris.
For Gaia DR1, the DPAC employed the INPOP10e ephemeris  (Fienga et al. 2013 ). Auxiliary observations. The Gaia photometric and spectroscopic data have to be tied to a physical flux and wavelength scale that is achieved through standard stars. In preparation of the Gaia mission, therefore, extensive observational campaigns to collect the relevant spectro-photometric data took place  (Pancino et al. 2012)  as well as monitoring campaigns to establish the photometric stability of candidate standard stars  (Altavilla et al. 2015). For the RVS, a compilation of radial-velocity standards was undertaken  (Crifo et al. 2010; Soubiran et al. 2013) , while for the solar system object processing, data on solar analogues had to be compiled to calibrate the interpretation of the Gaia photometry. A set of ∼30 Gaia FGK benchmark stars have been extensively studied to provide a reference for the astrophysical parameter inference system  (Jofré et al.
2014; Blanco-Cuaresma et al. 2014; Heiter et al. 2015; Jofré et al. 2015; Hawkins et al. 2016). One of the largest auxiliary observation programmes is the GBOT effort already mentioned in Sect. 5. 3. 2. Gaia Collaboration et al. : The Gaia mission Data publication. All the results from the DPAC processing are published as Gaia data releases and this is a specific task within the consortium. It encompasses the extensive scientific validation of the data (using the combination of all DPAC results), the documentation of the data processing and the results thereof, the incorporation of the data into a publicly accessible archive, and the provision of various tools to enhance the interrogation and scientific exploitation of the Gaia data, including pre-computed cross-matches to other large surveys  (Marrese et al.