dataesr/scientific-paragraphs-categorization huggingface dataset:
Intentionally, landcover and water pollutants have not been selected as attribute as they are the most sensitive to human induced alteration. The river network and its geometric characteristics was obtained from the River and Catchment Database derived from the Catchment Characterization Model (CCM, v2. 1)  46  , which contains spatially attributed information on slope, altitude, Strahler stream order, and catchment area (Extended Data Figure  2 ). Catchment geology was derived from the International Hydrogeological Map of Europe (IHME1500, v1. 2) at a scale of 1:500,000  47, 48  and its structured lithological attributes of bedrock formations. Rock formations have been classi ed into calcareous and siliceous types based on Water Framework Directive recommendations for stream classi cation.
Location of organic material was derived from the European Soil Database (ESDB, v2. 0) based on soil type and the soil parent material attributes of soil mapping units. Details regarding classi cation of geological formations are shown in Extended Data Table  6. Geological type has been attributed to river reaches based on location of its geometric center. Where data for river reaches were missing, values were derived from the nearest river segments upstream that had recorded information. Finally, climate related factors were obtained from the Environmental Strati cation of Europe  49  , where environmental zones have been delimited by modeling a set of environmental and climatic variables forming regions of homogeneous environmental conditions. Geographic data available for the entire study area were used to assure best available data consistency among regions.
Using pan-European datasets, however, often involves compromising on accuracy and precision. Even though we used the best available datasets, we are aware of several issues, which could affect our results but, which could not be resolved at the time of the analysis. In particular, river course precision and accuracy issues manifest themselves especially in the low-relief terrain by incorrect alignment in vertical and longitudinal dimensions. This could, in some cases, lead to the point that a river misses its correct out ow, and in the worst cases splits into two rivers owing in two distinct directions, entailing wrong catchment attribution  50. Low precision and accuracy of geology and soil data might have produced further inconsistencies with river network data. Some examples are depicted in Extended Data Figure  3.
Statistical analysis and extrapolation of results A two-step cluster and discriminant analysis was applied to establish the relationship between catchment environmental settings and expected sh macrohabitats (using observed sh community structure as a proxy). In the rst step, non-hierarchical cluster analysis was conducted using the Intercalibration data from 1099 river sites classi ed as Non-Disturbed Sites in the database. Cluster analysis was applied to two datasets sequentially. The rst dataset consisted of physical attributes of the sampling sites obtained from the broad-scale environmental attributes described previously. This was clustered into samples with similar habitat characteristics (Extended Data Figure  4 ). Cluster groupings were then added as an additional variable to the biological data on Habitat Use Guilds proportions at each site (obtained from the Intercalibration dataset of undisturbed sites) to produce a mixed data set of guilds/physical clusters (Extended Data Figure  5 ).
This second dataset was then clustered, resulting in one of 15 sh community macrohabitat type (FCMacHT) classes assigned to each sampling site. The Habitat Use Guilds proportions expected to occur within a river reach of a speci c habitat composition were determined using a Target Fish Community method for each FCMacHT. For each river type, 10 Non-Disturbed Sites (n) were selected at random, from which the sum of guild proportions was calculated and ranked, and the reciprocal rank calculated. The number of the sample sites is based on the metaanalysis of publicly available Target Fish Community reports from rivers (n=22) in New Hampshire (https://www. des. nh. gov/resource-center/publications?keys=tfcrpt). In these studies n was de ned using Multivariate Pseudo Standard Error (MultSE 51 ) and only rarely exceeded n=10.
Expected guild proportions were then obtained by dividing each guild reciprocal rank by the sum of the reciprocal ranks and plotted as pie charts for each FCMacHT (Figure  2 ). A distance matrix was created by standardizing the data using Gower and Manhattan similarity distances for the physical and mixed datasets, respectively  52, 53. The number of clusters was determined with the help of scree and silhouette plots. A partitioning around medoids (PAM) clustering model was applied  54. Cluster plots and silhouette plots, as well as box plots, were created for each variable. Subsequently, data discrimination with analysis of similarities (ANOSIM) was performed to verify model performance (Extended Data Figure  4 , Extended Data Figure  5 ). Finally, the relationship between macrohabitat type and broad-scale environmental attributes was analyzed to de ne a riverine macrohabitat typology for the entire European river network.
To this end, the FCMacHT class was added to the physical attributes of each undisturbed site as a grouping variable to perform classi cation and regression tree (CART) analysis  55  for determining how FCMacHT is distributed along the gradients for physical descriptors (Extended Data Figure  6 ). A complexity parameter plot was used to determine an acceptable relative error for pruning the decision tree. Following this, the decision tree was applied to all water bodies of Europe with speci ed environmental attributes to determine their FCMaCHT class (Supplement, Box 2). A summary of basic statistics of the Non-Disturbed Site dataset in comparison with the extrapolated dataset is shown in Extended Data Table  5. Statistics for each FCMacHT class are presented in Extended Data Table  6.
This was preceded by an extensive review of the literature with ndings discussed in a technical workshop for sh biology experts involved in the AMBER project  23. We identi ed 21 key riverine habitat attributes, which are important for aquatic organisms in rivers and likely to be modi ed by impounding. (Extended Data Table  7 ). These are: The summed HSI value in the bottom row of Extended Data Table  7  re ects the suitability of all listed habitat attributes for a given sh guild. This value is highest for specialised river guilds and lowest for generalists. To evaluate regional sensitivity of sh communities to habitat change, we weighted guild speci c suitability with the proportion of each guild for a given FCMacHT and divided by the maximum suitability value of 21, which assumes all attributes are very important.
Barriers impact on European rivers The AMBER Barrier Atlas 30 was used to identify existing barriers in Europe. The Atlas contains all barriers contained in public inventories, be it state-owned, international or regional. The barriers with known barrier type were attributed with the FCMacHT of the catchment they belong to, with assigned based on Table  1. Values of for each barrier were plotted spatially on the map and the median value used to color code catchments (Figure  5 ). Aggregate impounding length was determined for dams, weirs, and sluices with barrier height information. Barrier height was taken as the difference between bottom of the river bed and the lowest point on the crest of the barrier. The impoundment length was, in turn, estimated by dividing barrier height by slope. Total impoundment length for each barrier type was then corrected for barrier speci c reporting errors published in  8  , as well as missing barrier height data within the AMBER Barrier Atlas.
Acknowledgements This work builds upon research carried out for the  EU funded projects Adaptive Management of Barriers in European Rivers (AMBER)  and  Fishfriendly Innovative Technologies for Hydropower (FIThydro). Representatives of resource agencies of  Austria, Belgium, Bulgaria, Croatia, Czech Republic, England and Wales, Estonia, Finland, Greece, Hungary, Ireland, Lithuania, Luxemburg, Netherlands, Norway, Scotland, Slovakia, Slovenia, Spain, Sweden  and Poland gave their permission to use the sh biological data in JRC database. We would like to thank all members of  AMBER consortium  for their support and encouragement and particularly:  Victoria Hurst ,  Luca Borger  of  Swansea University, UK ;  Lucio Marcello of University of Highlands and Islands, UK ;  Sara Fernandez Fernandez of University of Oviedo, Spain ;  Peter Gough  of  World Fish Migration Foundation, Netherlands ;  Guillermo Giannico  of  Oregon State University , USA ;  Joshua Royte  of TNC,  USA ;  ClausTill Schneider  of  RWE Generation SE, Germany, Laura Wildman of Biohabitats, USA.
In this paper, we present SEM3D: a 3D high-fidelity numerical earthquake simulator that is tailored to predict the seismic wave field of complex earthquake scenarios from the fault to the epicenter site. SEM3D solves the wave-propagation problem by means of the spectral element method (SEM). The presented demonstrative test case was a blind M W 6. 0 earthquake scenario at the European experimental site located in the sedimentary basin of Argostoli on the island of Kefalonia (Western Greece). A well-constrained geological model, obtained via geophysical inversion studies, and seismological model, given the large database of seismic traces recorded by the newly installed ARGONET network, of the site were considered. The domain of interest covered a region of 44 km × 44 km × 63 km, with the smallest grid size of 130 m × 130 m × 35 m.
Regarding the first aspect, a new multi-tool numerical platform for generating 3D broadband source-to-site (BBS2S) earthquake scenarios is presented in Section 2. Figure  1  shows the scientific workflow of the BBS2S platform. The core of the platform is represented by the wave-propagation solver, a software called SEM3D  [42]  tailored to efficiently solve the wave-propagation problem by means of the Spectral Element Method (SEM, see Section 2. 1). SEM3D is complemented by a set of external libraries, either open-source or developed in-house, in order to construct the geological and seismological models. The computational improvements made in terms of BBS2S are the result of a synergistic effort of three French research teams at CentraleSupélec, Institut de Physique du Globe de Paris (IPGP), and the Commissariat à l'énergie atomique et aux énergies alternatives (CEA), mainly within framework of the French national project SINAPS@ (https://www.
SEM3D SEM3D is a high-performance numerical implementation of the SEM based on the original code called RegSEM (https://github. com/paulcup/RegSEM, accessed on 24 January 2022)  [28, 69] , which was forked and further developed by CentraleSupélec, the IPGP, and the CEA since 2014. SEM3D represents a competitive alternative to other flagship codes employing the SEM, such as SPECFEM3D  [53, 59, 70] , EFISPEC3D  [27] , and SPEED  [61]. SEM3D is a distributed-memory software (based on Message Passing Interface, MPI) written in Fortran 95 and C++, running on parallel MIMD (multiple instruction, multiple data) architectures. The computational domain Ω, duly discretized in N e hexahedral elements, is preliminary partitioned into N MPI subdomains (corresponding to the number of employed CPU cores) via a mesher that exploits the software library METIS 5.
For instance, Tang et al. [73] adopted HDF5 in order to efficiently tune parallel data compression and I/O for large-scale earthquake simulation, exploiting its data compression and chunking capabilities. In SEM3D, the I/O operations are shared by groups of 32 MPI processes and then separately written via HDF5. Given the diagonal mass matrix provided by the quadrature rules, the SEM is naturally prone to parallelization schemes  [74] : all local element matrices are computed independently, e. g. , as in the elements-first algorithms  [75]. In SEM3D, the time-marching Newmark's scheme is solved on each DOF (i. e. , at each GLL location on each element). The algorithm directly computes the product of the stiffness matrix by the displacement vector corresponding to F int in Equation (4). Therefore, the stiffness matrix is never assembled.
This latter advantage, along with the diagonal mass matrix, leads to the asynchronous communication scheme employed by SEM3D to iterate at each time-step. This setup naturally accommodates for viscoelastic and non-linear constitutive relationships: in SEM3D, a generalized Zener model is implemented  [76] , as much as a simple non-linear rheological model  [37]. The improvements made to the original RegSEM led to a considerable code speed-up, as Figure  3  shows. In  [50] , the author performed the verification and validation tests proposed by  [6]  for the Euroseistest Verification and Validation Project (E2VP) benchmark (the reference solution was uploaded on the SISMOWINE interactive seismological web interface used for numerical modeling benchmarking http://sismowine. org, accessed on 24 January 2022.
Meshing Chunks of the Earth's Crust: HexMesh The code makes use of a library called HexMesh (https://github. com/jcamata/HexMesh. git, accessed on 24 January 2022), which implements an efficient, linear, 27-tree finite-element mesh-generation scheme based on the previous works of  [88] ) and is capable of generating large computational grids (i. e. , ≈ 100 km) by extruding the provided Digital Elevation Model (DEM) and progressively coarsen it from top to bottom, so as to obtain a nonstructured grid (see, for example, Figure  6 ). HexMesh handles coastlines and bathymetries. 0. 1 , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , (a).
Modeling Soil Heterogeneity by Means of Random Field Modeling soil heterogeneity in large-scale domains implies a major computational effort: despite its characteristic dimension L (≈ 10 4 m), the computational grid must resolve both the wavelength and the heterogeneity-correlation length C-θ (≈ 10 2 m). Those large random fields can be effectively sampled over a coarse grid (with a step size relevant to the correlation length) and then interpolated onto the mesh of interest, provided by the GLL grid used in SEM3D. To generate random samples, an open-source library randomField (https://github. com/cottereau/randomField. git, accessed on 24 January 2022) was developed by de Carvalho Paludo et al. [39]  in the framework of BBS2S construction in order to be linked to SEM3D and feed it with large scale geological configurations with small random fluctuations.
[39]. Table  2  lists the results of the scalability test performed on randomField, with the standard and localized approaches, respectively (i. e. , by generating a unique random field over the entire domain (standard approach) or by generating independent realizations over subdomains (localized approach). This scalability test was performed by running the generation code on an Occigen cluster (on 2. 6 Ghz Intel Xeon Haswell dual-socket nodes with 12 cores each).
Modeling Extended Seismic Sources Fault discontinuities are modeled by means of a kinematic approach: the slip across the fault surface Σ (outward unit normal vector n Σ ) is interpreted as a pre-existing displacement discontinuity u (x; t) at a given surface. In practice, the kinematic assumption of extended faults consists in considering a distribution of double-coupled point-wise sources along the fault plane, with different time-varyng slip functions  [91]. A very realistic kinematic source model, namely, Ruiz's Integral Kinematic (RIK), was proposed by Ruiz et al. [92]. Specifically, the numerical simulations presented hereafter are based on the slip patches provided by the RIK implementation presented by Gallovič  [40] , called RIKsrf (https://github. com/fgallovic/RIKsrf, accessed on 24 January 2022).
However, fault modeling in this region is a major challenge because of the structural complexity and poor coverage by seismological, GPS and InSAR data  [96]. The site was originally chosen for its basin-like geological configuration, which makes it attractive to study 3-D site effects and surface waves. An extremely interesting amount of data regarding the area is fully documented in the NERA (Network of European Research Infrastructures for Earthquake Risk Assessment and Mitigation) project deliverable D11. 6 Comparison between data and numerical models (https://www. orfeus-eu. org/other/projects/nera/, accessed on 24 January 2022). Based on this state of the art, Touhami  [50]  constructed the numerical model used hereafter. Two possible realizations of the earthquake scenario are hereafter compared, run on two different supercomputers (Fusion and Occigen).
The low-frequency portion of the synthetic earthquake response undergoes slight modification when increasing the polynomial order of the underlying shape functions, mostly due to different spatial interpolations of the source points within each element. This aspect suggests great care when transitioning to the higher-frequency range and when introducing heterogeneous material properties. One realization of the seismic scenario (both in terms of source characteristics and random fluctuations of the material properties) cannot be considered exhaustive: however, this paper shows the feasibility of this strategy exploiting physics-based earthquake simulators to span the scenario and uncertainty space in an almost blind predictive framework. SEM3D can be effectively used to generate hybrid broad-band synthetic ground motion and to estimate the intra-event and inter-event residuals of ground-motion prediction equations, in areas of low-moderate seismicity, such as France, where a poor database can prevent the definition of representative ground-motion prediction equations.
Acknowledgments: The present work is placed within the framework of the French research project  SINAPS@ (Séismes Installations Nucléaires, Améliorer et Préserver la Sûreté ). This project has been initiated by the members of the institute SEISM after the nuclear disaster of Fukushima (Japan, 2011). The present work is part of the work package  Non-Linear Sites Effects and Soil-Structure Interaction  [49].
, 2021). While this recent study mostly focused on evaluating dietary patterns among adults, there are some interesting observations among the subadults (age <5 years) not discussed. Figure  2  shows the bulk collagen δ 13 C and δ 15 N values of all humans from the two sites, plotted against the local dietary sources, as represented by mixing polygons. The bulk collagen C and N measurements from all human and faunal samples are obtained from a published report  (Cheung et al. , 2021) -with the exception of one new data point from BLR (BLR09) (Dataset S1). Mixing polygon is a good way to help identify possible missing source group(s)  (Cheung & Szpak, 2021). Each polygon is a convex hull formed by the mean δ 13 C and δ 15 N values of the known major protein sources from each respective site, after adjusted for trophic enrichment factor (TEF).
| METHODS AND MATERIALS All individuals were aged using established osteological methods  (Moorrees et al. , 1963; Scheuer & Black, 2000; Schmitt, 2005) , as described and reported in  Thomas' report (2011). Summary biological information of all human samples involved are provided in Dataset S1. The procedure for collagen extraction, collagen quality control, and bulk collagen stable isotope measurement are reported in  Cheung et al. (2021). For the selected 10 individuals, an aliquot of extracted collagen was sent to the Stable Isotope Facility at University of California, Davis for CSIA. Samples were prepared and analyzed following the procedures developed by Yarnes and Herzsage  (Yarnes & Herszage, 2017). A brief description of the procedure is as follows: collagen was first hydrolyzed in HCl, and derivatized as N-acetyl methyl esters (NACME).
The AA derivatives were then separated on an Agilent DB-35 capillary column, and analyzed in duplicates by GC/C/IRMS, using a Thermo Trace GC 1310 gas chromatograph coupled to a Thermo Scientific Delta V Advantage IRMS via a GC Iso-Link II Combustion interface. Measurements are reported in "per mil (‰)" and are calibrated to VPDB for δ 13 C and AIR for δ 15 N, using certified standard reference materials from USGS, NIST, and the IAEA. Measurement accuracy and reproducibility was monitored by a series of internal reference materials with known isotopic compositions, including a matrix-matching material-GEL (gelatine). Total measurement error was less than 1. 0‰ for both carbon and nitrogen isotope analyses. Detailed measurements and standard deviations, including those of the control reference materials, are provided in Dataset S2.
Published data from several groups of humans, broadly categorized as C 3 terrestrial consumers or high freshwater protein (HFP) consumers, are also included as comparison  (Honch et al. , 2012; Naito et al. , 2013; Webb et al. , 2018). The references for these comparing data are outlined in Table  2 , and the data is provided in Dataset S3. While it is certainly not ideal that other than the δ 15 N AA values from Noyen-sur-Seine, no CSIA data from other local fauna is avail- able. An inter-site comparison of all faunal types (e. g. , herbivores, omnivores, carnivores) showed that all fauna separate reasonably well according to their feeding habits (Supplementary Information Figure  S3 ), regardless of their provenance or time period. Furthermore, as noted previously, one major advantage of CSIA is that this approach allows researchers to evaluate TP and baseline nutritional sources directly without the need to first characterize the local isotopic baseline.
| Compound specific isotope analysis The summary biological information and results from selected components for the 10 individuals analyzed for compound specific isotope contents are listed in Table  4. Figure  5  shows all human data plotted against faunal data. The complete set of carbon and nitrogen isotope measurements of each AA for all data used in this study (original and previously published) is provided in Dataset S3. Carbon isotope ratios in phenylalanine (δ 13 C Phe ) and the difference between phenylalanine and glycine (Δ 13 C GlyÀPhe ) are used to illustrate the sources of carbon in the diets of the BLR and VPB individuals (Figure  5a ). Nitrogen isotope ratios in glutamic acids/glutamine (δ 15 N Glx ) and phenylalanine (δ 15 N Phe ) are used to highlight the TP of the BLR and VPB individuals (Figure  5b ).
T A B L E 3 Summary statistics of bulk bone collagen δ 13 C, δ 15 N, and δ 34 S values of adults and subadults at BLR and VPB. All data are from published report  (Cheung et al. , 2021)  BLR VPB δ 13 C (‰) δ 15 N (‰) δ 34 S (‰) δ 13 C (‰) δ 15 N (‰) δ 34 S (‰) Adult À20. 9 ± 0. 3 (n = 23) +10. 9 ± 0. 5 (n = 23) 6. 6 ± 2. 9 (n = 12) À21. 1 ± 0. 1 (n = 19) +12. 1 ± 0. 5 (n = 19) 7. 7 ± 1. 2 (n = 12) Subadult À21. 0 ± 0. 3 (n = 23) +12. 0 ± 1. 5 (n = 23) 4. 8 ± 1. 9 (n = 12) À21. 3 ± 0. 8 (n = 10) +13. 2 ± 1. 4 (n = 10) 7. 5 ± 1. 1 (n = 8) Abbreviations: BLR, Balloy; VPB, Vignely. T A B L E 4 Biological information and isotope measurements of the 10 individuals analyzed for compound specific isotope contents. TP refers to trophic positions, calculated using the formula described in Chikaraishi et al.
ACKNOWLEDGMENTS This research was funded by the  Agence Nationale de la Recherche  (projet NEOGENRE  ANR-17-CE27-0023 , directed by A. Thomas https://anr. fr / Project -ANR-17-CE27-0023 ). We thank  Dr. Christopher Yarnes  from  UC Davis Stable Isotope Facility  for conducting the compound specific isotope analysis,  Dr. Takumi Tsutaya  from the  Graduate University for Advanced Studies, S okendai , for his assistance in using WARN, and  Dr. Eric Guiry ,  Cara Kubiak ,  Bill Radford , and  Megan Wong  for their helpful comments. Special thanks to the Associate Editor, the  Editorial Board Member , and the two anonymous reviewers for their thorough comments on the drafts of this manuscript.