dataesr/scientific-paragraphs-categorization huggingface dataset:
Current and Future Species Geographic Distributions A niche modeling approach based on environmental data was used to generate species presence/absence maps in the Mediterranean Sea and drive species spatial distributions in OSMOSE  (Moullec et al. , 2019). Environmental predictor variables, i. e. , temperature, salinity, were extracted from the World Ocean Atlas 2013 version 2 2  which provides observed climate data over the 1975-2012 period. To take into account the vertical distribution of species in the water column, six environmental metrics were derived from monthly temperature and salinity climatologies: mean sea surface temperature and salinity (0-50 m depth), mean vertical temperature and salinity (0-200 m depth), and mean sea bottom temperature and salinity (50 m -maximum bathymetry depth).
Current geographic distributions were modeled using an ensemble forecasting approach involving eight climate suitability models embedded in the freeware BIOMOD2 R package  (Thuiller et al. , 2009; R Core team, 2015)  (see  Moullec et al. , 2019  for details on models parameterization and assumptions). The niche models developed and calibrated under present conditions were then used to project the environmental niche of species to the 2021-2050 and 2071-2100 periods using future environmental predictors. A threshold approach maximizing the fit with current species distribution was used to predict the geographical range of the species under both current and future environmental conditions. In this study, only the current and future geographic distributions of species already present in the Mediterranean Sea before the year 2013 and for which the biological knowledge necessary for the parameterization of the OSMOSE model is available were modeled.
Future Plankton Productions The same deltas approach was followed for the biogeochemistry forcing extracted from Eco3M-S: anomalies between the historical and future time periods  (2021-2050 and 2071-2100)  were calculated and applied to current plankton biomasses. Transient biogeochemical simulations were performed over historical  and future (2006-2100, RCP8. 5 scenario) periods using the Eco3M-S model, forced by the physical model CNRM-RCSM4. The historical simulation was initialized using the MEDAR-MEDAtlas database  (Manca et al. , 2004)  as in  Kessouri (2015). The final state of this simulation was then used to initialize the scenario simulation. Terrestrial, atmospheric inputs and nutrient concentrations in the Atlantic have been kept constant from 1950 to 2100. An average of nutrient loads over the period 1960-2000, based on regional estimates by  Ludwig et al.
Projected Changes of Catch Annual fisheries catches simulated by OSMOSE amounted to 788 043 t for the current period. By 2021-2050, under RCP8. 5 and "business as usual" fisheries management, the total projected catches in the Mediterranean Sea are expected to remain stable (Figures  7A, B ). By the end of the century, the total catches could rise by ca. 7% to reach 840 008 t. However, this projected increase  hides a substantial heterogeneity between species and between management units (i. e. , Geographical Sub-Areas; GSA). By the middle of the century, simulated catches showed either a downward trend in most GSAs (up to -22% in South Tyrrhenian Sea (GSA 10), -9% in Balearic Island and in Southern Adriatic Sea (GSA 5 and 18, respectively) or -7% in South of Sicily (GSA 16), or a relative stability (i.
ACKNOWLEDGMENTS The authors acknowledge the  Pôle de Calcul et de Données Marines (PCDM)  for providing DATARMOR computational resources (http://www. ifremer. fr/pcdm)and the  CALMP computation center  (Grant  P1331 ) for the HPC resources, and the support of the SIROCCO team (http://sirocco. obs-mip. fr/). The authors also acknowledge the  CNRM  for making the physical data available. The climatic simulations used in this work were downloaded from the Med-CORDEX database (www. medcordex. eu).
FUNDING FM was funded by a  Ph. D. grant from the  French Ministry of Higher Education, Research and Innovation. This work was partially funded by the  USBIO  project of the  LabEx CeMEB , an  ANR  " Investissements d'avenir" program  ( ANR-10-LABX-04-01 ) and the  SOMBEE project of the joint BiodivERsA and Belmont Forum call "BiodivScen 2018 " ( ANR-18-EBI4-0003-01 ).
Indeed, parametric descriptions help to compare two distinct populations; in addition, they can be useful for random generation (see for instance DREAM. 3D (Groeber and Jackson, 2014)  or NEPER  (Quey et al. , 2011) softwares). Rhines and Patterson (1982)  have reported that in many recrystallized polycrystalline materials, the grain volume follows a lognormal distribution; thus, the equivalent grain radii follow a lognormal distribution too. Lopez-Sanchez and Llana-Fúnez (2016)  have proposed the so-called two-step method, which consists in fitting a parametric distribution on the histograms given by the Saltikov method; the previous authors have used the lognormal distribution as the underlying distribution. However, the Saltikov method depends on the number of classes (or bins) of the histogram; thus, the results may be user-dependent.
IMPLEMENTATION All the methods detailed in the previous sections have been implemented in MATLAB R. Improper integrals (see Eqs. 4 and 9) were computed using the built-in integral command whereas the trapezoidal integrations in Eqs. 11 and 16 were computed using the cumtrapz and trapz commands, respectively. The latter were performed using m = 1000 equally spaced integration points between r min and r max. The MDE was performed using the fminsearch command. This command uses the simplex method  (Lagarias et al. , 1998)  to perform minimization. It is efficient when the investigated space is small (low value of z) and if the cost function has no local minima  (Nelder and Mead, 1965).
MATERIAL Uranium dioxide (UO 2 ), introduced in a previous paper  (Depriester and Kubler, 2019) , has been used as a test material. Its microstructure was imaged by Electron Backscattered Diffraction (EBSD) mapping and the EBSD data were processed to reconstruct the grains using of misorientation threshold of 5 •. Grains cropped by the region of interest were removed from the data, resulting into n = 4264 individual grains. Fig. 2  illustrates the final dataset. Based on their area, their apparent equivalent radii were computed with respect to Eq. 1. Fig. 3  gives the 2D histogram of the apparent radii. In addition, the empirical CDF, computed from Eq. 7, is plotted in this figure. One may notice the threshold effect due to the resolution of the EBSD data. Indeed, the step size was 2 µm; thus, the minimum area was 4 µm 2.
As a result, the equivalent radius, as defined in Eq. 1, was always larger than 1. 128 µm. It is worth mentioning that the quality of the original EBSD data allows to consider all grains, even if some of them contain only one pixel. Indeed, the frequency at lower values in Fig. 3  appear reasonably correct ; in other cases, a minimal size may be introduced. The following parametric distributions are investigated: monodisperse, uniform, positive normal, lognormal, Gamma, Weibull and Rayleigh. All those distributions and their related parameters are detailed in Table  1. The Rayleigh distribution is a special case of the Weibull distribution (with k = 2). Wicksell (1925)  has shown that its transform (Eq. 3) keeps the Rayleigh distribution unchanged (i. e. , its PDF is an eigenfunction with eigenvalue 1).
COMPARISON WITH OTHER STEREOLOGY METHODS The results from MDE can be compared with those from other stereology techniques, namely the Saltikov method  (Saltikov, 1967) , the two-step method (Lopez-Sanchez and Llana-Fúnez, 2016) and the threestep method  (Depriester and Kubler, 2019). Fig. 6  schematically illustrates the workflow to be used in order to utilize those techniques. In order to compare the results from MDE to those from the Saltikov method, the latter has been applied on the example dataset Ω n. Since there is no general rule about the number of bins to be used in the Saltikov method (denoted N hereafter), the latter has been applied with 10 to 30 bins. In each case, the CvM test (O), as defined in Eq. 16, has been computed. It is worth reminding that the Saltikov method gives the unfolded radius distribution; thus, it must be refolded before computing the CvM test.
INFLUENCE OF THE SAMPLE SIZE It has been shown before that the normal distribution leads to the best fit with respect to the CvM criterion; thus, only this distribution will be used hereafter. The sample used here is composed of 4264 grains. Hence, this dataset has been randomly downsampled in order to investigate the influence of the sample size (denoted n above) on the accuracy of the results. For each investigated value of n, 100 different random samplings have been done, from which the MDEs have been performed. As a result, 100 values of R m , 100 values of σ and 100 values of κ have been found for each value of n. In each case, the mean values and the standard deviations of those sets have been computed. It appears that the mean values of R m and σ are almost constant when n > 1000. In addition, when n > 1000, their standard deviations are small, compared to the mean values.
Study sites and seismic experiments We use seismic recordings from three seasonally deployed networks in the ablation zones of two temperate Alpine glaciers and of the GIS. Each of the acquired datasets presents different patterns of seismic wave fields corresponding to the three configurations investigated for GF estimate retrieval, as defined in the introduction. All networks recorded varying numbers of near-surface icequakes (blue dots in Fig. 2a-c ). Different processing schemes were used to constitute the icequake catalogues and are detailed in Appendix A. In this study we only use vertical component data of ground velocity to generate vertical-to-vertical crosscorrelation functions which primarily contain the Rayleigh wave fundamental mode  (Shapiro and Campillo, 2004). Some of the datasets involve surface seismometers whose horizontal components are regularly shifted over the course of the melting season.
Glacier d'Argentière array The Argentière seismic array (Fig. 2a ) was deployed in late April 2018 and recorded for 5 weeks. It consists of 98 threecomponent surface sensors regularly spaced on a grid with a 350 m × 480 m aperture and a station-to-station spacing of ∼ 40 m for the along-flow profiles and ∼ 50 m for the acrossflow profiles. This large N-array experiment used the technology of nodes (Fairfield Nodal ZLand 3C) that combine a geophone, digitizer, battery, data storage, and GPS in a single box  (Hand, 2014)  and allowed a rapid deployment within a few hours. ZLand geophones have a natural frequency of 5 Hz and recorded continuously at a sampling rate of 500 Hz. Besides seismic sensors, four on-ice GPS instruments were deployed. At the array site, the ice is 80-260 m thick  (Hantz, 1981)  and flows at an approximate rate of 0.
(c) Frequency-velocity diagram obtained from f-k (frequency-wavenumber) analysis of NCC in (a). The dispersion curve of phase velocity for Rayleigh waves and P waves is plotted as black dots. The dashed blue line shows the frequency-dependent resolution limit, given the maximum wavelength and sensor spacing λ max = max /2. Black lines are theoretical dispersion curves for fundamental mode Rayleigh wave velocity computed for ice thickness of either 150 or 250 m with Geopsy software. We used the elastic parameters for the ice and bedrock as given in  Preiswerk and Walter (2018, Sect. 6. 1). The same figure for icequake cross-correlations is available in Appendix (Fig. B2 ). space, averaging the NCC in regular distance intervals on a dense array deployment helps the GF estimate convergence. The stacked section of ICCs (Fig.
Dispersion curve inversion and glacier thickness estimation Sensitivity of Rayleigh waves obtained on NCC to frequencies below 5 Hz enables us to explore the subsurface structure with inversions of velocity dispersion curves. Due to the general noise source locations up-flow and down-flow of the network, we limit our analysis to receiver pairs whose accurate GF could be obtained. We thus compute the dispersion curves on eight along-flow receiver lines which constitute the array (inset map in Fig. 5 ). For each line, we invert the 1-D ground profile which best matches seismic velocity measurements in the 3-20 Hz frequency range, using the neighborhood algorithm encoded in the Geopsy software  (Wathelet, 2008). Following  Walter et al. (2015) , we assume a two-layer medium consisting of ice and granite bedrock.
(2012)  to locate weak-amplitude subsurface sources, and  Chmiel et al. (2015)  used it for microseismic data denoising. Also,  Seydoux et al. (2017)  and  Moreau et al. (2017)  showed that the SVD-based approach improves the convergence of NCC towards the GF estimate. Here, we combine MFP and SVD in order to remove spurious arrivals in NCC caused by the moulin located within the GIS array and thus improve the GF estimate emergence. typically starting in the afternoon hours. These events occurred on 29 d out of the 45 d total monitoring period. Signal intensity and duration depended on the days of observations, and the energy was mostly concentrated in the 2-10 Hz range within distinct frequency bands (Fig. 1b ). Röösli et al. (2014)  and  Röösli et al. (2016b)  showed a clear correlation between water level in the moulin and start and end times of the tremor; therefore the tremor signal is referred to as "moulin tremor".
The CSDM is a square matrix with a size equivalent to the number (N = 13 for the GIS array) of stations (N-by-N matrix). MFP is a forward propagation process. It places a "trial source" at each point of a search grid, computes the model-based GF on the receiver array, and then calculates the phase match between the frequency-domainmodeled GF and the Fourier transform of time-windowed data. The optimal noise source location is revealed by the grid points with maximum signal coherence across the array. In order to calculate the MFP output, we use 24 h data of continuous recordings on 27 July which encompass the moulin tremor. We calculate a daily estimate of the CSDM by using 5 min long time segments in the frequency band between 2. 5 and 6 Hz, which gives in total M = 288 of segments for 1 d.
Discussion The three methods proposed in this study could theoretically be applied to each one of the explored datasets but were not further tested here. The standard processing schemes proposed in Sect. 3 for computations of NCC and ICC successfully work on Glacier d'Argentière as we benefit from favorable seismic illumination patterns and redundancy of the measurements on the dense array. The need of spatial averaging of the correlation responses in regular distance intervals could potentially be overcome when (1) stacking the GF over a longer time range of typically weeks or months (e. g. Sabra et al. , 2005; Larose et al. , 2008)  or (2) applying more advance processing schemes such as the one proposed for GIS and Gornergletscher studies, to overcome anisotropic source distribution effects.
CWI should be appropriate for smaller icequake datasets (here we employ the method on a loop of 700 events, i. e. less than 7 % of the icequake catalogue used in Argentière) and smaller size arrays to be able to record a coherent coda across the network (typically 250 m wide). Such arrays can be deployed in targeted regions where pervasive crevasses are present, i. e. typically near the glacier margins. As an example, CWI could help to estimate the GF at smaller subarrays at the edges of a larger array as in the configuration of the Glacier d'Argentière experiment. In the following we discuss the type of array deployment, geometry, and measurements suitable for structural and monitoring studies using the GF obtained by either one of the processing methods described above.
The prior dispersion curve has to be as close as possible to the measured dispersion curve to avoid cycle skipping during the fitting. In our procedure, we take as a starting model the average dispersion curve obtained from f-k analysis of the cross-correlation section computed at the Argentière array. For sparse network configuration (as in  Greenland and Gornergletscher, i. e. Figs. 9d and 11c) , we take the theoretical dispersion curve computed with Geopsy software and based on the prior knowledge we have on the subsurface. The dispersion curves are then modeled as polynomial functions to enforce their smoothness and to reduce the number of fitting parameters and help the convergence of the fitting process. The order of the polynomial can be varied. Usually a polynomial order of 5 is a good compromise between smoothness and complexity of the dispersion curve.
(c) Frequency-velocity diagram obtained from f-k (frequency-wavenumber) analysis of NCC in (a). The dispersion curve of phase velocity for Rayleigh waves and P waves is plotted as black dots. The dashed blue line shows the frequency-dependent resolution limit, given the maximum wavelength and sensor spacing λ max = max /2. Black lines are theoretical dispersion curves for fundamental mode Rayleigh wave velocity computed for ice thickness of either 150 or 250 m with Geopsy software. We used the elastic parameters for the ice and bedrock as given inPreiswerk and Walter (2018, Sect. 6. 1). The same figure for icequake cross-correlations is available in Appendix (Fig. B2).
Acknowledgements. We gratefully acknowledge all the people involved in the  RESOLVE  project and who participated in the array deployment in Argentière and collected and processed raw formats of seismic data. We thank  Olivier Laarman ,  Bruno Jourdain ,  Christian Vincent , and  Stéphane Garambois  for having constructed bed and surface DEMs using ground-penetrating radar for the bed and drone data for the surface. We also thank  Léonard Seydoux  for insightful discussions on MFP and SVD. We acknowledge the constructive comments from the editor  Evgeny Podoloskiy ,  Naofumi Aso , and the anonymous reviewer. Financial support. This work was funded by the  Swiss National Science Foundation (SNSF)  project  Glacial Hazard Monitoring with Seismology  (GlaHMSeis, grant  PP00P2_157551 ). Additional financial support was provided to AS by the  Swiss Federal Institute of Technology (ETH Zürich).
Code and data availability. Seismometer data from Gornergletscher and GIS are part of the 4-D glacier seismology network (https://doi. org/10. 12686/sed/networks/4d, SED, 1985)  archived at the Swiss Seismological Service and can be accessed via its web interface http://arclink. ethz. ch/webinterface/ (last access: January 2020; Swiss Seismological Service, 2020). Argentière data are hosted at ISTerre. Access can be granted by request to Philippe Roux (philippe. roux@univ-grenoble-alpes. fr) or Florent Gimbert (florent. gimbert@univ-grenoble-alpes. fr). ObsPy Python routines (http://www. obspy. org, last access: February 2020;  Beyreuther et al. , 2010)  were used to download waveforms and process icequake catalogues. NCCs of the Argentière dataset were computed using the MSNoise Python package (http://www.
msnoise. org, last access: November 2018;  Lecocq et al. , 2014). Author contributions. PR and FG designed the Argentière experiment in the scope of the RESOLVE project (https://resolve. osug. fr/, last access: May 2019). FL, PR, FW, and AS processed the icequake catalogues. AS processed the correlation functions and analyzed the results at Argentière. MFP of Greenland data was processed by MC. CWI at Gornergletscher was processed by AS and FW with the input of JC. Icequake correlations by MDD were processed by FL. AM provided the code for computing phase velocities by fitting the cross spectrum with a Bessel function. AS and MC prepared the manuscript with contributions from all co-authors.
Deep relationships and the sequence of divergence among major lineages of angiosperms (magnoliids, monocots and eudicots) remain ambiguous and differ depending on analytical approaches and datasets used. Complete genomes potentially provide opportunities to resolve these uncertainties, but two recently published magnoliid genomes instead deliver further conflicting signals. To disentangle key angiosperm relationships, we report a high-quality draft genome for the soursop (Annona muricata, Annonaceae). We reconstructed phylogenomic trees and show that the soursop represents a genomic mosaic supporting different histories, with scaffolds almost exclusively supporting single topologies. However, coalescent methods and a majority of genes support magnoliids as sister to monocots and eudicots, where previous whole genome-based studies remained inconclusive.
2010; Moore et al. 2011; Zeng et al. 2014) ; or 3) magnoliids sister to monocots  (Nickrent and Soltis 2006). Each of these topologies were inferred from organelles (chloroplast and mitochondrial loci) and/or nuclear datasets, with variable levels of support depending the analytical method and taxonomic sampling used. In parallel, recently published studies challenge our understanding of the early evolution of angiosperms. Reconstruction of the ancestral angiosperm genome shows a reduction in the number of chromosomes between the divergence of the putative sister taxa of all remaining angiosperms Amborella and the most recent common ancestor (MRCA) of eudicots  (Murat et al. 2017). In addition to this assessment of early angiosperm genomic features, Sauquet et al. (Sauquet et al. 2017)  suggested that the early evolution of angiosperm flowers was marked by successive reductions in the number of whorls of both the perianth and the androecium.
Results To assemble the genome of the soursop, we extracted DNA from a soursop tree (Annona muricata) grown in Xishuangbanna Tropical Botanical Garden  (XTBG, Menglun, China), and generated 131. 43 (164. 47x) and 36. 95 Gb (46. 24x)  of Illumina and PacBio reads, respectively (Supplementary Table  1 ). SOAPdenovo 2. 04 was used to assemble the first draft of the genome, reaching a contig N50 of approximately 700 kb. The soursop assembly was further refined using both 10X Genomics data ) and Bionano optical mapping data (95. 9 Gb -120. 01x) (Supplementary Table  1 ) to improve the scaffold N50 to 3. 4 Mb, the longest scaffold being 20. 46 Mb (GC content of 34. 35%, Supplementary Table  2 ). The total assembly length of the soursop genome was 656. 78Mb (scaffold assembly). Scaffolds longer than 100kb totaled 646.
64Mb (98. 45% of the total length). This level of contiguity is similar to the one obtained in Liriodendron chinensis (N50=3. 5 Mb  (Chen et al. 2019) ) but smaller than obtained in Cinnamomum kanehirae (N50=50. 4Mb after Hi-C scaffolding  (Chaw et al. 2019) , and better than other genomes assembled at scaffold-level (e. g. (Yang et al. 2018; Arimoto et al. 2019; Zhang et al. 2019)). A total of 444. 32 Gb of data were produced using Illumina, PacBio, 10X Genomics and Bionano technologies, corresponding to 556x coverage of the soursop genome. We assessed the quality of the soursop genome assembly by mapping back the Illumina reads against the assembly. A total of 97. 16% reads can be mapped, covering >99. 92% of the genome, excluding gaps. 99. 81% of the genome was covered with a depth >20x, which guaranteed the high accuracy of the assembly for SNPs detection (Supplementary Table  3 ).
We assessed conflicting signals in the genome of the soursop (Fig. 2 ), using three dataset. Firstly, we used a set of 2426 orthologs identified in the quartet (Annona muricata, Arabidopsis thaliana, Amborella trichopoda and Oryza sativa), maximising the number of loci. Secondly, a set of 689 orthologs identified in the 12 species was used for comparative analyses as described above, maximizing the number of species. Finally, a set of 1578 orthologs identified in the previous quartet, plus Cinnamomum kanehirae and Liriodendron chinense, was used maximizing the magnoliid representation. Using the set of 2426 orthologs and single-gene ML phylogenetic reconstructions, we show that the SMT topology found using Cinnamomum as a representative of magnoliids, is only supported by ~16. 8 % of the genes (29) with a clear evolutionary signal (SH-like values >0.