dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 2. Surface traces of the fault system using DInSAR Spaceborne Differential SAR interferometry (DInSAR) has been widely used during the past decades to track land subsidence or uplift related to groundwater extraction or underground gas storage  41. The same method has been also successfully used for identifying ruptures due to earthquakes and quantifying the co-seismic motion  42, 43. We particularly aim to refine the location of ruptures with a particular interest on La Rouvière fault (LRF) and the surrounding ones mapped on the 1:50 000 geological maps  36. To carry out this analysis, four interferograms were produced using SAR data from the Sentinel-1 mission (Method section and.
Differential SAR Interferometry (DInSAR) The displacements are estimated along the sensor's Line of Sight (LOS), which is the sensor-to-target direction. DInSAR measures the projection of real motion along the LOS and provides 1D displacement measurements. Those measurements are relative in space and time: they are spatially related to a reference point, and temporally to the date of the first available satellite acquisition. Four interferograms were produced using Sentinel-1 data (Table  S1 ). The processing is based on the Gamma processing software (https//gamma-rs. com). In order to interpret these interferograms for identifying and quantifying surface ruptures, an unwrapping additional step is required. For this step, we used the Minimum Cost the BRF rupture (Figure  3 ). In order to obtain additional candidates we added positions of faults from the 1:50 000 geological map (see Figure  S1 ).
These faults were imported in the tool for profiles stacking and displacement estimation included in the Cosi-corr software  53, 54. Lateral profiles are automatically generated by the software perpendicularly to the fault candidate (20 on LRF and 20 + 3 added manually on BRF). Our objective is first to validate points on the fault candidate as reliable observations if significant differential motion between each side of the profile is observed and then to quantify this motion. In addition, if the "jump" on the displacement profile is not exactly on the candidate's position this procedure allows to adjust the position by displacing the candidate accordingly to the jump's position. Figure  S2  illustrates the use of the tool on the south-west of the LRF. Finally, the obtained points are connected in order to obtain a continuous rupture trace.
Hydraulic simulations by (Table  S4 ). The water content or soil moisture content is the quantity of water The hydraulic parameters in the Barremian / Urgonian limestones are highly variable in the host rock, the damaged zone and the core fault  [37] [38] [39] [40]. In the Urgonian carbonates at Russel (https://lsbb. cnrs. fr/, about 90 km southeast of Le Teil), the observations show the presence of discontinuities (joints, veins, faults and stylolites) that influence the hydraulic properties from core to reservoir scale  38  : the porosity varies from 1% to 20% and the permeability varies in a range between 10 -17 m 2 and 10 -11 m 2. These hydraulic parameters are used by the ComPASS platform  45, 46  (https://github. com/BRGM/ComPASS) for the reference cases (Table  1 ) and the sensitivity cases (Table  S4 ).
Seismological data analysis at Clauzel House (CLAU) The data recorded at Clauzel House (CLAU) are made available to the scientific community by the quarry owner LAFARGE CIMENTS. The sensor is a threecomponent, short-period seismograph (sampling rate at 1056. 4 Hz), installed in a private house to monitor the vibrations due the quarry blasts. The recorded data of the mainshock include visually unnatural jumps in velocity and this leads to unexpected level of acceleration. After visiting the station CLAU, we observed that the station have not been correctly fixed on the house floor and probably may have been impacted by the fall of miscellaneous objects around. Although the whole waveform may not be exploitable, the first movement at the beginning of the signals could be informative  35. In order to verify the correct polarity, we check the blast signal of the 25 th September 2019 for which the origin is known (Ev1 in Figure  8 ).
For the given records, we remove the linear trend, apply the Butterworth bandpass filter (order of 8) between 1 and 10 Hz and integrate once using the n o n -p e e r r e v i e w e d software SeisGram2K Seismogram Viewer v7. 0. 0X10 (www. alomax. net) for data viewing and processing. Then, we exploit the particle motion for a selected time window manually (Figure  S9 ). We obtain a back azimuth of N98°E ± 20° for the true value of N111°E. Thus, the particle motion indicates approximatively the event direction with a margin of error of around 15°. We thus use the data from the same station to estimate the direction of the mainshock of the 11 th November 2019 and its associated uncertainty. n o n -p e e r r e v i e w e d.
The 3-day products for ascending overpasses are used between Mai 2015 and December 2019 (Figure6) and the 10-day aggregated products for descending overpasses are used between March 2010 and December 2019 (FigureS6). As the residual water content  is almost zero for SMOS acquisitions, the normalized water content (or effective saturation Se) depends only on the volumetric water content SSM and the porosity ω (about 50% at 5-10 cm in the studied area given by the Harmonized World Soil Database):=  Hydraulic parameters(matrix, fault).
Acknowledgments This research was conducted as part of a research partnership co-funded by  BRGM  and the quarry owner LAFARGE CIMENTS (grants  CF19DRP21  and  CF21DRP01 ). We thank  LAFARGE CIMENTS  to make available the extracted volumes of rocks from  Le Teil  quarry during the period 1850-2019 and the data of the SC03 geotechnical borehole drilled in 2016. A. Bu  warmly thanks his former PhD supervisor  Laurent Charlet  for the field visit looking for possible water sinkholes around the epicenter location and the visit of his nearby house at Saint-Thomé damaged by the earthquake.
The Scuola di Equitazione is a military building, represented in the historical geological and topographic maps, and still present along the Flaminia road (nowadays called "Caserma Cap. Camillo Sabatini; Reggimento Lancieri di Montebello 8˚";  Fig 2). Surveying the archival documentation, we found a historical picture in the data bank of the Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA, Institute for Environmental Protection and Research), published in an Italian daily magazine named "L'Ilustrazione Italiana"  [40]. The picture shows the then King of Italy, Vittorio Emanuele III, who went to ride a horse in the proximity of the riding school near the Flamina road, as reported in the caption (Fig  3 ). On the background of the photo, above on the left (Fig  3A ), an old tower (named Tor di Quinto) can be observed, which is still present today and located at the crossroad between two streets, Via Tor di Quinto and Via del Casale di Tor di Quinto (light blue circle in Fig  3C ).
3D Models In order to obtain a virtual surface and highlight the morphology and the distribution of the clasts embedded on it, a 3D model of has been carried out by close-range photogrammetry. Due the fragility and the dimension of the specimen, the photos were taken in the museum, without moving the specimen, directly on its base, using the "walk-around" technique described by  [57]. 65 photos were taken and imported in the Agisoft Metashape software at the Polo Museale Sapienza, in order to recreate a digital version of the surface, which initially consisted of a dense cloud of 13 million points. The 3d shape of the mandible was created at high quality and consists in an open surface composed by more than 1. 300. 000 faces. The final 3D model was imported into Blender software where it was scaled and then exported as an obj file.
Petrographic analysis Five samples were analyzed to characterize the sand composition of the sediment fraction recovered during restoration work on the surface of the Hippopotamus skull. The results were compared with newly acquired petrographic data from nine sediment samples collected from three different outcrops within the Roman basin (Fig  6 ). The outcrops were selected to characterize the sediment signature of the three Formations identified as the potential sedimentary units hosting the Hippopotamus skull. In particular, three samples per unit were collected from fluvial conglomerates, sand and silt deposits and fluvial-palustrine calcareous and volcanoclastic sands and silt levels belonging to the Fosso della Crescenza (FCZ), Valle Giulia (VGU) and Vitinia (VTN) Formations in the Tor di Quinto (North of Rome) and Mezzocamino (South of Rome) areas (Fig  6 ).
The compositional biplot display point count data collected on sand samples and the main compositional parameters (rays; Q, total quartz; K, K-feldpsar; P, plagiocase feldspar; Lc, carbonate lithics; Lm, metamorphic lithics; Ls(silic), siliciclastic lithics; Lv, volcanics; Pyr, pyroxene phenocrists phases). The length of each ray is proportional to the variability of the parameter in the data set; the angle between two rays reveals whether the corresponding parameters are well correlated (0˚), uncorrelated (90˚), or inversely correlated (180˚)  [59, 60].
Rays represent the parameters in the datasets (Q, total quartz; K, K-feldpsar; P, plag; Lc, carbonate lithics; Lm, metamorphic lithics; Lv, volcanics; Hm, dense minerals). The length of each ray is proportional to the variability of the parameter in the data set; the angle between two rays reveals whether the corresponding parameters are well correlated (0˚), uncorrelated (90˚), or inversely correlated (180˚) (from  [64] ). Note in A) that the modern upstream samples cluster with the Hippopotamus, VGU, and FCZ samples; conversely volcanoclastic downstream fluvial samples cluster with VTN sand. LmLvLs ternary diagram comparing the ancient sand lithic fragments composition. Note that the hippopotamus sediment samples overlap the lithic signature of the VGU sand (C). https://doi. org/10. 1371/journal.
Fossil specimens of Hippopotamus antiquus housed in the University Museum of Earth Science, Department of Earth Sciences,   Faure (1985)  were not considered in this work because the measurements were taken differently from  [16]  and later studies. Measurements were taken to the nearest 0. 1 mm with a digital caliper. A Principal Component Analysis (PCA) was conducted on the cranium of Tor di Quinto, Pleistocene and extant hippopotamuses (Table  1  in S1 File). Only a few cranial measurements of Pleistocene hippopotamuses are available in paleontological literature. In order to include the large number of specimens in the PCA, we selected eight cranial variables: HopA, HBA, BfM, BN, Botot, Boc, BTl and Bz. Considering the limited number of specimens in the dataset, the values of the first component (PC1) have been chosen as independent variables in the ANOVA model (Analysis of Variance model) to explore possible differences among groups.
The digital investigation of the outer surface of the left hemimandible shows clearly the presence of these depressions, whose size ranging from 0. 5 to 5 cm, generally with a 1 to 2. 6 cm depth (Fig  5 ). This indicates that during the depositional process small pebbles encrusted the bone of the MPUR/V 149, affecting the preservation of the outer surfaces. All these considerations support that MPUR/V 149 fossil was collected in a sandy deposit with small pebbles (ranging from 0. 5 to 5 cm). The petrographic data obtained to characterize sediment composition recovered inside the cavities of the Hippopotamus skull were compared with sand detrital modes from the modern and ancient deposits sourced by the Tiber River system to obtain direct information about the sediment provenance and the stratigraphic context of the enclosing sedimentary unit.
Acknowledgments BM thank  Linda Riti  and  Michele Macrı  `for the access to the hippopotamus skull from Tor di Quinto ( Rome, Italy ) and other fossil collections of the MUST, and support during its study. BM is thankful to  Riccardo Castiglia  and  Edoardo Di Russo  for granting access to the extant hippopotamus material stored in the  Museo di Anatomia  comparata " Battista Grassi ",  Sapienza Università di Roma , and their kindness and availability. We are grateful to  Fabian Console  for her supporting in study of the archive documentation of the,  Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA, Institute for Environmental Protection and Research). We would like to thank two reviewers,  Neil F. Adams  and  Darı ´o Fidalgo , for their comments and suggestions.
In the Alps, the metamorphic transitions (e. g. , blueschist to eclogite) are likely to be imaged by tomography because they occur as sharp to tens of kilometer-wide boundaries (e. g. , the LT to the HT blueschist-facies transition in the Schistes Lustrés (SL) unit,  Herviou et al. , 2022). The interpretation of these images and the determination of the rocks composing the crustal top of the Alpine panel require independent knowledge of the seismic velocities of the rocks potentially involved. Several worldwide databases of crustal rock seismic properties exist in the literature (e. g. ,  Barruol & Mainprice, 1993; Lloyd et al. , 2011; Rudnick & Fountain, 1995; Rudnick & Gao, 2003)  and were recently used in the Alps  (Malusà et al. , 2021). However, the exact position of metamorphic transitions and associated velocity contrasts strongly depends on the chemical composition of rocks (e.
g. ,  Almqvist & Mainprice, 2017). The use of generic databases is hence an oversimplification that only gives first-order insights. Therefore, we need studies based on local analogs to refine our interpretations. To this end, it is crucial to build a regional catalog of quantified seismic properties, considering chemistry, pressure, temperature and metamorphic equilibration. The existence of such a catalog would be a precious tool to detect and identify the reaction fronts and infer the most probable lithologies constituting the crustal top of the Alpine slab at depth. Initial interpretations of the NRP-20 WEST and ECORS CROP profiles proposed a duplex of LC beneath the internal Alps  (Schmid & Kissling, 2000). The reinterpretation of the same transects in the light of new tomography results  (Diehl et al.
Thermodynamic Modeling Phase diagrams for a fixed bulk composition (i. e. , pseudosections) were calculated using the Gibbs free energy minimization procedure of de  Capitani and Brown (1987)  with the software THERIAK/DOMINO (de Capitani and Petrakakis, 2010) and the database tcdb55cc2d. bs, mainly composed of the Holland and Powell (1998) database and its variants. The following solution models were used: chlorite  (Holland et al. , 1998) , talc, staurolite, cordierite  (Holland & Powell, 1998) , white mica  (Coggon & Holland, 2002) , orthopyroxene (R. W. White et al. , 2002) , feldspar  (Baldwin et al. , 2005) , ilmenite, garnet, biotite, spinel and liquid (R. W. White, et al. , 2007) , amphibole  (Diener et al. , 2007) , omphacite, clinopyroxene  (Green et al. , 2007). Carpholite and olivine were considered as ideal solutions.
Seismic Properties Modeling Within a PT Domain Rock seismic velocities (V p , V s , and V p /Vs) within a pressure-temperature domain were calculated from single-crystal physical properties (database from  Abers & Hacker, 2016). The rock mode was translated into volume percentage and mineralogical phases representing less than 1% were removed. The finite Eulerian strain was calculated from the pressure and the material parameters by assuming the third-order finite strain equation using the  Bina and Helffrich (1992)  equation. Thermal expansivity, density, bulk and shear modulus were then extrapolated over a pressure range from 0. 25 to 2. 5 GPa and a temperature range from 100°C to 1,000°C, allowing the eventual estimates of V p and V s. Phases for which elastic parameters are not known were substituted by close end-members with available parameters (see Supporting Information S1 for more details).
PT profile calculations were performed according to the previous scenarios. For conversion of pressure to depth, we only considered lithostatic pressure along the slab and we calculated it as follows: 𝑃𝑃 = 𝑔𝑔 * 𝑧𝑧 ∑ 0 ∆𝑧𝑧 * 𝜌𝜌𝑧𝑧 with densities from the CIFALPS density model previously used for fitting gravity data  (Zhao et al. , 2015). Melts are produced in 5 lithologies (0. 5 wt% in the Canavese mylonitic blueschist, 0. 16 wt% in the Val Sesia metagabbro, 0. 08 wt% in the Taou Blanc chlorito-schist and 0. 02 wt% in the Val Sesia gneiss) along the HT profile. Although we acknowledge that the presence of melt could have a tremendous impact on effective wave velocities  (Hacker et al. , 2014) , the present calculations are solely based on the solid rock part. The absence of present-day volcanism, the limited heat flow in the western Alps, and the age of their youngest magmatic intrusions (26-24 Ma for Novate granite,  Lustrino et al.
Role of Anisotropy in Seismic Velocity Calculations The velocity step at 45 km depth in the S-wave tomographic model is located in the subducted slab of the European LC at a local increase in the slab dip (Figures  1  and 2 ). The bending of a seismically anisotropic slab is known to induce distortion in receiver functions (e. g. ,  Schneider et al. , 2013; Shiomi & Park, 2008). In a teleseismic P-wave tomography model, the illumination of structures is not identical in the three directions. The present data set is based on ambient noise shear wave velocity estimates. Thus, the structures are imaged from the shear wave component of horizontally propagating Rayleigh waves. Regarding this, it is reasonable to inquire whether the velocity increase, suggested as the transition from amphibolites to granulites in the rocks, could be attributed to the influence of rock anisotropy.
Environmental factors Climatic information (i. e. , mean annual precipitation and temperature) were extracted from the WorldClim database (https://www. worldclim. org/data/index. html), and averaged values of climatic parameters from recent years (2013-2021) were used in this study. Plant cover was determined at each site based on three transects across 30 m × 30 m plots. Information on the population was obtained from the latest available city censuses using official national statistical sources  33. GDP and HDI per capita (over 1990-2015) in the regions for the cities surveyed were extracted from the reported dataset  40  , providing information on the economic activity and key dimensions of human development for each location. Moreover, we collected information about mowing, irrigation, and fertilization treatments by surveying the managers of urban greenspaces  33.
Analyses of microbial functional traits In total, 54 composite topsoil samples (one composite sample per plot) from 27 typical paired urban and natural areas were selected for metagenomic analyses. Sufficient amounts (roughly 500 ng) of microbial DNA were extracted using the DNeasy PowerSoil DNA Isolation Kit (QIAGEN Inc. , Germany) according to the manufacturer's protocol. Shotgun sequencing was performed using an Illumina HiSeq (Illumina Inc. , USA) at Majorbio in Shanghai, China. Raw reads (PE150, 150 bp pairedend reads) were trimmed to remove low quality reads. The SeqPrep software (https://github. com/jstjohn/SeqPrep) was used to remove adapter sequences. Then, the library sickle (https://github. com/najoshi/sickle) was used to trim the reads from the 5′ end to 3′end using a sliding window (size 50 bp, 1 bp step).
All reads below the quality threshold of 20 were trimmed. The resultant reads below 50 bp or containing N (ambiguous bases) were discarded. The trimmed reads were then mapped against the protein sequence of the BacMet (http:// bacmet. biomedicine. gu. se/) and Subsystem Technology (MG-RAST; https://www. mg-rast. org)  52 , respectively. MG-RAST generates taxonomic assignments based on the SEED subsystem database by DIA-MOND software (version 0. 9. 32) by best-hit classification with a maximum E-value of 1e -5 , a minimum identity of 60%, and a minimum alignment length of 25 amino acids for proteins and functional categories. The resulting table was parsed at SEED Subsystem Level 3 by the software SUPER-FOCUS. The relative abundances of annotated genes were calculated using the number of reads mapping to genes for each sample.
Information on variables included in the model is either measured in situ or in the laboratory, or by collecting data from the literature as explained above. The SEMs allowed us to determine the direct and indirect effects of multiple environmental factors on soil contaminants. The probability that a path coefficient differs from zero was tested using bootstrap resampling. Doing this instead of assessing significance with frequentist methods allowed us to overcome limitations related to the data matching a particular theoretical distribution. The data matrix was fitted to the model using the maximum-likelihood estimation method. There is no single universally accepted test of the overall goodness of fit for SEM. Thus, we used the Chi-square test (χ 2 ; the model has a good fit when 0 ≤ χ 2 / d.
o. f ≤ 2 and 0. 05 < P ≤ 1. 00) and the root mean square error of approximation (the model has a good fit when 0 ≤ RMSEA ≤ 0. 05 and 0. 10 < P ≤ 1. 00). The SEM analyses were performed using AMOS 21. 0 (SPSS Inc. , Chicago, IL, USA). In parallel, we evaluated the relative importance of individual natural and human factors on the four types of soil contaminants by a mixed-effects model using the "glmulti" package in R. The importance of each predictor was expressed as the sum of Akaike weights for models. We used a cutoff of 0. 8 to differentiate between essential and nonessential predictors as done in a previous study  57. We also used Spearman's correlation analyses to evaluate associations between selected factors and soil contaminants and the response ratios. We explored associations between potential soil contaminants and the selected functional genes, and observational correlations are essential to understand ecosystems and develop new hypotheses  3.
Acknowledgements We thank the researchers involved in the  MUSGONET  project for collection of field data. This study was supported by a 2019 Leonardo Grant for Researchers and Cultural Creators,  BBVA Foundation (URBANFUN) , and by the  BES  grant agreement No  LRB17\1019  (MUSGONET). We are grateful for the assistance of  Yunyun Hao  and  Xuemei Han  during soil sampling. We also thank  Drs. Shuai Du  and  Xiuli Hao  for their help in data analyses. M. D-B. is supported by the projects from the  Spanish Ministry of Science and Innovation  ( PID2020-115813RA-I00 ) (SOIL4GROWTH) and  TED2021-130908B-C41  (URBANCHANGE) funded by  MCIN/AEI/ 10. 13039/501100011033 , and a project of the  Fondo Europeo de Desarrollo Regional (FEDER)  and the  Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía  ( FEDER Andalucía 2014-2020 Objetivo temático  " 01 -Refuerzo de la investigación, el desarrollo tecnológico y la innovación ") associated with the research project  P20_00879 (ANDABIOMA ).
Y-R. L. is supported by the  National Natural Science Foundation of China  ( 42177022 ). M. G. A. H is supported by the  Swiss National Science Foundation  ( 310030_188799 ). D. J. E. is supported by the  Hermon Slade Foundation. F. B. and J. L. M. acknowledge support from the  Spanish Ministry and FEDER  funds for the project  AGL2017-85755-R , the  I+D+i  project  PID2020-114942RB-I00  funded by  MCIN/AEI/10. 13039/501100011033 , the i-LINK+2018 ( LINKA20069 ) from  CSIC , as well as funds from " Fundación Séneca " from  Murcia Province  ( 19896/GERM/15 ). E. M. -J. was supported by an  Experienced Researcher Fellowship  of the  Humboldt Foundation. E. M-J. and C. P. acknowledge support from the  Spanish Ministry of Science and Innovation  ( PID2020-116578RB-I00 ). F. A. is supported by  ANID   FONDECYT 1220358.