dataesr/scientific-paragraphs-categorization huggingface dataset:
The NESS data, however, is significantly off the zero baseline, and often positively biased. This indicates that, despite the choice of  ℎ , both the GMMs under-predict the ground-motions of events in NESS datasetalthough the unrevised  Kotha et al. (2020)  GMM with  ℎ = 6. 2 appears to performs slightly better with a smaller bias. The between-event standard-deviation  0 of the two GMMs is about 0. 45 at all periods (see Fig. 7 ); which means, although most NESS events are within ± 0 at short periods, at long periods most are significantly above + 0. The misfit between both the GMMs and the NESS events is a concern. But it is important to note a few assumptions made in this comparison: 1) the  , 0 of the ESM events are corrected for specificities of the tectonic locality in which they originate using the 2  , and of attenuating regions through which their shear-waves propagate using the  3, (more details in  Kotha et al.
(2020b)  provided an   dependent heteroskedastic model of  0 for use with the implementation of (revised)  Kotha et al. (2020)  GMM in the regionally adaptable ESHM20 backbone logic-tree. While that heteroskedastic model of  0 is adapted from Al  Atik (2015) , which was developed from a global dataset of events, here we provide one using only the ESM events. Alongside a heteroskedastic  0 ~   for the ESM events, we also examine the impact of adding NESS events to ESM events. Fig. 12  shows the investigation of  0 ~   for two cases:  , 0 of only the ESM events,  , 0 of ESM events along with   of NESS events. We perform a simple test for heteroscedasticitythe Breusch-Pagan test  (Breusch and Pagan 1979 )programmed into an R package lmtest by  Zeileis and Hothorn (2002). The Breusch-Pagan test fits a linear regression model to the residuals of a linear regression model (here  , 0 ~   ) and rejects the null hypothesis of homoscedasticity if too much of the variance (here  0 ) is explained by the additional explanatory variables (here   ).
0 is the test statistic of the Breusch-Pagan test, and follows a chi-squared distribution with the number of regression parameters (here   ) as degrees of freedom (here 1). Therefore, if  0 has a p-value below an appropriate threshold (e. g. < 0. 05) then the null hypothesis of homoscedasticity is rejected and heteroscedasticity is assumed. homoscedasticity, but simply a lower significance of heteroscedasticity. In this case, we observe that the heteroscedasticity is most significant for both ESM and ESM&NESS datasets in the range 0. 2 <  < 2. Further, heteroscedasticity of ESM events'  , 0 is stronger than when including also the   of global, tectonically diverse, events of NESS. As seen in Table. 1, one of the reasons a few NESS   ≥ 6 events have large  thereby increasing the scatteris likely because of their origin in a tectonically different environment (region), and style of faulting compared to those in Italy, Greece, and Turkey.
For the moment, we provide a readily applicable  0 (  ) for the ESM events, and an experimental one for ESM&NESS data: 𝜏 0 (𝑀 𝑊 ) = { 𝜏 0,𝑀 1 = 𝜏 0 𝑀 𝑊 < 𝑀 1 = 5 𝜏 0,𝑀 1 + (𝑀 𝑊 -𝑀 1 ) ( 𝜏 0,𝑀 2 -𝜏 0,𝑀 1 𝑀 2 -𝑀 1 ) 𝑀 1 ≤ 𝑀 𝑊 < 𝑀 2 = 6. 5 𝜏 0,𝑀 2 𝑀 𝑊 ≥ 𝑀 2 (6) In equation (  6 ), at each period (, not shown in equation),  0, 1 is the  0 (  ) for events with   <  1 = 5 , kept equal to  0. For   ≥  2 = 6. 5 ,  0 (  ) is a smaller  0, 2. For  1 ≤   <  2 ,  0 (  ) decreases linearly from  0, 1 to  0, 2. Values of  0, 1 are the same for ESM and ESM&NESS datasets, but  0, 2 values are different, as seen in the lower-panels of Fig. 12. In addition to the heteroscedasticity of between-event variability, we have examined the magnitude dependent heteroscedasticity of residual variability; i. e. the so-called within-event  , residuals of NESS data containing the various regional and site-specific effects, and the aleatory residuals  of ESM data corrected for region-, locality-and site-specific effects by  Kotha et al.
(2020). Once again, although  , and  are not technically the same, they are both the left-overs after filtering out all possible the fixed-and random-effects from observations. Supplementary Fig. S7  is similar to Fig. 12  shown above for between-event random-effects. Therein we observed that the  , of NESS data show strong heteroscedasticity at long periods. However, unlike the decreasing variability of   with increasing   , long period  , ( ≥ 1) variability increases rapidly with increasing. We understand this to be due to the anisotropy of shear-wave radiation, pulse-like effects, etc. in near-source ground-motions. We believe it is necessary to investigate these effects in more detail than that can be presented here. Therefore, for the moment, we will not further discuss heteroscedasticity of within-event  , residuals of the NESS dataset.
g. local magnitude   , body-wave magnitude   , etc. ) Once the GMM coefficients were revised to have lower within-model uncertainty on large magnitude predictions at near-source distances, we evaluated its performance against a high-quality near-source strong ground-motion dataset. Unlike the regressed ESM dataset however, the NESS dataset contains ground-motion data collected from tectonically diverse, global, large magnitude earthquakes recorded by dense network of stations located on particularly soft-soil geology. In addition, a large fraction of NESS data are characterised by pulse-like characteristics. Owing to its peculiar sampling, NESS was decided against integration into ESM; in order to not bias the isotropic  Kotha et al. (2020)  predictions with anisotropic pulse-like ground-motion observations.
We noticed that most of the large magnitude events in NESS have produced stronger ground-motions than that the GMM predicts. The misfit between NESS observations and GMM median predictions is larger at longer periods, where pulse-like effects and soil conditions play a major role in aggravating ground-motions. At short periods, the misfit was within one standard-deviation, and that's an improvement we could achieve by revising the model with  ℎ = 5. 7 instead of  ℎ = 6. 2 of  Kotha et al. (2020). For the sake of brevity however, we have only demonstrated that between-event random-effect and aleatory residual variabilities become larger if NESS data is integrated into ESM. To evaluate these estimates would require us to impose the same level of non-ergodicity (event, site, tectonic locality, and attenuation region) on NESS data as that was possible on ESM datasetbut was not possible.
DATA AND RESOURCES The European Strong Motion flatfile is available at https://esm. mi. ingv. it//flatfile-2018/ with persistent identifier PID: 11099/ESM_flatfile_2018. The NESS flatfiles are available at http://ness. mi. ingv. it/. The analyses in this study have been performed in R software  (Team 2013 ). In addition to those cited in the maintext, we used the libraries dplyr (Wickham et al. 2019b), ggplot2 (Wickham et al. 2019a), ggmap  (Kahle et al. 2019) , viridis  (Garnier 2019) , etc.
It is recommended that future evaluations of model error schemes in three-dimensional ensembles take into account weather types, vertical levels and variables that are directly sensitive to these schemes. Field experiment data and instrumented observation sites may help to better understand and design model perturbation schemes, through precise comparison between vertical profile observations and meteorologically consistent numerical simulations, such as the single column runs used here, or more sophisticated numerical models including large-eddy simulations. Doing so would allow a better understanding of model error representation issues than the impact studies such as the one presented here, by indicating what is physically right or wrong in model perturbation schemes. This study has used a very simplified modelling framework, so it can only be used to understand specific aspects of the ensemble perturbation, namely, those that depend on vertical interactions in the boundary layer over a few hours.
Acknowledgements This study was funded by the  French Government through Météo-France ,  CNRS  and the  University of Toulouse. It received the  IPSIPE research grant  from the  LEFE/MANU programme of CNRS/INSU. The LANFEX case set-up used data that was kindly provided by  I. Boutle  and  J. Price. The manuscript was improved by the comments and suggestions of two reviewers.
Other origins of S-MIF bearing ice-core sulfate may be possible  [47] [48] [49] [50] 53. Evi ence of S-MIF has been found in aerosol samples in present-day polluted urban and semi-urban environments  [47] [48] [49] [50]. In such locations, mineral-dust-associated sulfate has been proposed to exhibit S-MIF  47, 50  , yet inherent mechanism(s) remain unclear and highly speculative. However, no such dust events have ever been reported for the period studied here  54  , as also evidenced from the record of calcium concentrations (a marker for dust) in our dataset (Figure  S1a ; see Methods as well). Other than these, large-scale circulations such as strong El Niño Southern Oscillation (ENSO) have led to a positive Δ 33 S anomaly in Antarctic snow pit samples  53. The positive values were then suggested to likely arise from carbonyl sulfide (COS) photochemistry following a strong stratosphere-troposphere exchange  53.
While 10 Be was not measured for corresponding sections of the ice cores used in this study, it has been measured on Vostok 3G cores in the past  28. The record is, however, discontinuous and of poor resolution  28. The same is available for Vostok 4G cores but for a resolution of ~100 years  58. 10 Be measured on Vostok 5G cores offered a similar time resolution (~10 years as used in this study)  29. However, a ~ 10 m offset in depth has been found between the 3G and 5G records  29. Taking this offset into account, we note that the 10 Be concentrations were, on average, ~ 290±50. 10 3 atoms/g during this period (Figure  S4 ). The corresponding ice-core 10Be measurements are unavailable for the multiple SN event. Therefore, we used another widely employed cosmogenic tracer Δ 14 C. The measurements are from the global repository, the CHRONO IntCal20 Database, developed from various achieves (e.
Acknowledgments We thank  Elsa Gautier  and  Patrick Ginot  for their support with lab-based operations. We also thank  Jean-Robert Petit , colleagues  Volodya Lipenkov  and the  Russian Vostok site consortium  for their field endeavors and for allowing access to the VK ice core archive and repository. This study was supported by the  Marie Skłodowska-Curie Action  Grant number  1010180  (to S. D. ; hosted by J. S. and G. P. ) and  CNRS-INSU Les Enveloppes Fluides et l'Environnement (LEFE)  grant " REVA " (to G. P).
g. Baele et al. , 2019; Belousova et al. , 2002; Bouzari et al. , 2016; Bruand et al. , 2016; Kempe and Gotze, 2002). In the sedimentary environment, the study of apatite chemistry can provide clues about element sources and depositional/early diagenetic conditions (e. g. McArthur and Walsh, 1984; Shields and Stille, 2001; Kidder et al. , 2003; Zhu et al. , 2014; Chen et al. , 2015; Alvaro et al. , 2016). Such studies were performed on various phosphate-rich beds hosted by Neoproterozoic to early Cambrian sedimentary rocks of the Yangtze Craton. Among these, trace element and Sr-Nd isotope data obtained on phosphatic rocks of the Neoproterozoic (Ediacaran) Doushantuo Formation point to a crustal/detrital source for the REE, phosphogenesis in an anoxic environment and distribution of the REE modified by diagenetic fluids  (Xin et al.
7 nm spectral resolution. Spectra were acquired and processed using the Spectragryph optical spectroscopy software (https://www. effemm2. de/spec tragryph/). Quantitative microanalyses of major element concentrations (Electronic Supplementary Material, Item 3) were acquired using a JEOL JXA 8800L electron microprobe at the Institute of Geography and Geology, University of Würzburg (Germany). It was operated at 15 kV and 20nA, with a beam diameter of 10 µm. This microprobe is equipped with four wavelength-dispersive (WDS) spectrometers and standard LDE1, TAP, PET and LIF crystals (LiF for F, Fe 2 O 3 for Fe, SrSO 4 for Sr). Mineral standards (vanadinite for Cl, apatite for P and Ca, and andradite for Si) supplied by CAMECA (SX Geo-Standards) were used for reference. The Lα line was used for the measurements of Sr and Ba, and the Kα line for all other elements.
Indeed, the contents of these elements in micas and sulphides are commonly very low and negligible compared to their concentration in apatite (e. g. Tillberg et al. , 2019; Villaseca et al. , 2020; Zeng et al. , 2015; Zhao et al. , 2021). The precisions were better than 10 % (rel. ) for all REE. Data treatment was done using the software "Iolite"  (Paton et al. , 2011) , following  Longerich et al. (1996)  for data reduction. More information is provided in the Electronic Supplementary Material (Item 1). The methodology for Sr isotope analysis is presented in Electronic Supplementary Material, Item 6. A summary of the method is provided here, the extended method is part of the Electronic Supplementary material, Item 1. The Sr isotope composition from the apatite grains was measured at the Microgeochemistry Facility, Department of Earth Sciences, University of Gothenburg.
There, an ESI 213 NWR laser ablation system is coupled to an Agilent 8800 ICP-QQQ (see details in  Zack and Hogmalm (2016)  and  Hogmalm et al. (2017) ). Data were collected during four analytical sessions. Circular laser spots with different diameters were used during individual runs. A repetition rate of 10 Hz was used for all runs and the fluence varied between 5. 9 and 7. 3 J/cm 2. Helium was used as carrier gas and mixed with nitrogen before introduction into the ICP-MS/MS; N 2 O was used as reaction gas and introduced overnight into the reaction chamber prior to analyses. Additional He was supplied during two of the four analytical sessions. NIST SRM 610 was used as primary reference material for the calculation of the 87 Sr/ 86 Sr ratios and BCR-2G to test accuracy, precision and reproducibility between the analytical sessions.
Acknowledgments The authors would like to express their gratitude to  Ulrich Schüssler  and  Stefan Höhn  for their assistance in acquiring electron microprobe analyses at the  University of Würzburg. Chantal Peiffert  is warmly thanked for help in acquiring LA-ICP-MS analyses. The authors thank  Thomas Goovaerts  for helping to improve the English of the manuscript. The research was partly funded by the  Belgian Federal Science Policy Office (BELSPO) -BRAIN program  (DESIRED projects). The  LA-ICP-MS laboratory from GeoRessources  was partly funded by the  French National Research Agency  through the  national program  " Investissements d'avenir" of the Labex Ressources21  with the reference  ANR-10-LABX-21-RESSOURCES21. This is also a contribution to the  Strategic Research Plan of the Czech Geological Survey  ( DKRVO 2018 ( DKRVO -2022 )).
Acknowledgements We thank  D. -Y. Jiang  ( Peking University ),  J. -D. Huang  ( Anhui Geological Museum ),  M. Manabe  ( National Museum of Nature and Science, Tokyo ),  F. Wu  ( Institute of Vertebrate Paleontology and Paleoanthropology ),  Y. Kobayashi  ( Hokkaido University ), and  J. Liu  ( Hefei University of Technology ) for help with the investigation of fossil collections under their care. O. Dülfer  ( University of Bonn ) and  H. Matsuoka  ( Kyoto University ) are also acknowledged for their help with fossil and thin-section preparation. We thank  T. Kumagae  ( Japan Petrology ),  K. Asato  ( Fukui Prefectural Dinosaur Museum ),  M. S. Arkhangelsky  ( Saratov State University ), and  Y. L. Bolotsky  ( Institute of Geology, Russian Academy of Science ) for their assistance with field investigations.
Testing the robustness of breccia type identification: a statistical approach Field investigations helped to distinguish seven different types of breccias (i. e. , types 1, 2bis, 2, 3, 4, 5 and 5bis), based on sedimentological, structural, and petrographical characteristics. In this paragraph, our objective is to provide a quantitative validation of the separation between breccias we made in the field using a qualitative approach. We hypothesize that some of the different types may be genetically linked. The significance of the seven field-base types is tested by performing an Agglomerative Hierarchical Analysis (AHC) based on the main sedimentological and petrographical characteristics (see part III for methodological details). The Principal Coordinates Analysis (PCOa), based on the same dataset, is used to reveal petrographical similarities between the clusters identified by the AHC.
g. ,  Vauchez et al. , 2013; Clerc et al. , 2015)  although the detachment surface was not included in these previous reconstructions of the contact between the Agly Massif and the Boucheville basin. The dominant brittle deformation documented in the granulitic crust (and inferred from the serpentinized peridodites in Salvezines) across the Belesta detachment is analogous to the latest stage of crustal thinning when the upper crust is coupled to the lower crust, allowing the exhumation of the mantle, as suggested by seismic data from the Iberian margin (e. g. ,  Sutra and Manatschal, 2012). Observations from the Iberian margin also indicate that pre-rift strata are kept mechanically coupled to the basement above the detachment exhuming the mantle. This contrasts with our observations in the Agly Massif that suggest the contact between the pre-rift cover and the basement remains tectonically active during the whole extension process, notably in the hyper-extended part of the riftwhere the mantle is exhumed.
We do not exclude that the cover-basement decoupling may have been reactivated but no data lend support for a Cenozoic origin for breccias. The minor role played by the salt layer during tectonic inversion is in line with the dominant thick-skinned style of shortening in the Agly Massif. The study of breccia systems has also highlighted the major role played by the evaporitic Triassic particularly during the first stages of rifting as a major decoupling level at the basement/cover interface on the necking zone of the rifted domain that later evolved as extension increases into a main detachment system leading to exhumation of deep crust and mantle. The long-term evolution of crustal thinning was accompanied by salt tectonics and shearing assisted by the circulation of fluids reflected in intense hydrofracturing of the base of the Mesozoic cover.
, 2019) ,  (Zou and Abdelkhalik, 2021) , but for the present study, the potential theory method is preferred to a high-fidelity model for computational cost reasons: the sea conditions studied are within the linear range and the sea state dataset analyzed is wide. The time series of the free surface, the forces acting on the hull, the hull motions, and also the forcing acting on the mooring are obtained for monodirectional and multidirectional sea states. This study uses a spread catenary mooring system  (Niosi et al. , 2021) : it is a passive system with the only purpose of station keeping and the device efficiency is minimally influenced  (Martinelli et al. , 2012). The paper is organized as follows: in Section 2 the cases study of the site and the device are presented; Section 3 illustrates the methodology employed in the numerical simulations.
The different analyses are specified in Section 4. Finally, Section 5 discusses the results and concludes the paper. In this article, the wave climate of the Mediterranean Sea is initially investigated to identify a specific suitable case study for the analysis. To this end, the wave information are acquired by ERA5  (Hersbach et al. , 2020)  database of ECMWF (European Center for Medium-Range Weather Forecasts). ERA5 products combines model data with observations from around the world, offering hourly time series that spans diverse environmental data; those concerning the wave are provided with a spatial resolution of 0. 5°× 0. 5°. Since the aim of the paper is to quantify and compare the results produced between monodirectional and multidirectional analyses, the coarse-scale used is considered adequate.
Then, the multidirectional spectra are defined as the combination between the JONSWAP frequency spectrum  (Hasselmann et al. , 1973)  and the cos -2s directional spectrum  (Krogstad and Barstow, 1999). The Deterministic Amplitude Scheme (DAS) method is used to compute the directional sea elevation components  (Mérigaud and Ringwood, 2017)  and the interaction of the waves with the moored hull is investigated with OrcaFlex, a validated software for the static and dynamic analysis of floating structures  (Johanning and Smith, 2008; Massana Hugas et al. , 2014; Harnois et al. , 2015; Paduano et al. , 2020).
CFD is discarded as a possible numerical model, as the computational cost would be too high. Furthermore, the linear potential theory is sufficient to provide reliable results for the slope of small waves and small body movements. Indeed, CFD models are the most suitable tool for extreme wave loads when strong non-linearities are present, while, in this study, the dynamics of PeWEC under normal operating conditions are analyzed. Therefore, the OrcaFlex marine dynamics software, developed by Orcina Ltd. and implementing potential theory, is selected for the numerical simulations. OrcaFlex is used extensively for modeling WECs, and its validation is confirmed by multiple comparisons between experimental results and numerical simulations  (Johanning and Smith, 2008; Massana Hugas et al. , 2014; Harnois et al.
We present GNU Data Language (GDL), an open-source free incremental compiler for programs written in Interactive Data Language (IDL*) and Precision Visuals -Workstation Analysis and Visualization Environment (PV-WAVE**), two computer languages used for scientific data analysis. GDL is highly compatible with the IDL and PV-WAVE and aims to run any existing IDL codes without any modifications. GDL comes with its dedicated IDE GDL Workbench and Jupyter kernel gdl_kernel to provide a comfortable development environment. In addition, GDL supports interoperability with Python. GDL is freely available at https: //gnudatalanguage. github. io/. * IDL is a registered trademark of L3HARRIS.
Statement of Need Interactive Data Language (IDL) is a commercial, domain-specific language used for processing data in various scientific fields, including astronomy, geosciences, biology, hyperspectral, medical imaging  (Burrage et al. , 2021; Castro-Tirado et al. , 2021; Coates et al. , 2019; Lee et al. , 2022; Xiao et al. , 2022) , and even COVID-19 research  (Chen et al. , 2021). Although IDL is not so widely used these days for scientific computing with the rise of modern computing languages, many legacy IDL codes are still being used. Undoubtedly, porting such legacy codes to modern languages would require a huge effort; it is much more economic to run them with IDL. To run such IDL codes without any financial constraints, there have been several efforts to create a freeware clone of IDL, including Fawlty Language (https://www.
flxpert. hu/fl/). Fawlty Language is a very successful implementation as it supports nearly 100% of the latest IDL 8. 0 syntax. However, Faulty Language is neither open-source nor free as in free speach and thus users cannot report or fix problems in its source code. Neither IDL nor FL fulfill modern requirements for freely reproducible research. The GDL project is an international effort to create a free software clone of Interactive Data Language (IDL) or Precision Visuals -Workstation Analysis and Visualization Environment (PV-WAVE), preserving the capability to run the vast body of scientific legacy codes without any technical, legal or financial constraints. GDL has been developed for over four decades with public funding throughout academic institutions around the world. The initial development was done by Marc Schellens almost 20 years ago (the commit history preserved on GitHub dates back to 2004) and since then, continuous development has been carried out by an evolving team of volunteer contributors -both freelance and affiliated with academic institutions (A.