dataesr/scientific-paragraphs-categorization huggingface dataset:
Third, we preserve a comprehensive set of metadata associated with each site-level datum that enables filtering by a variety of arbitrary thresholds (e. g. , number of samples, within-site scatter). Finally, with site-level data, state-of-the-art paleomagnetic statistical tests (e. g. ,  Heslop & Roberts, 2018)  can be applied to the data sets associated with each pole. We focused our site-level case study on Cenozoic North America (0-60 Ma), given the large volume of data previously published. Our compilation, which includes 1,796 site-level records extracted from 39 studies (Figure  1 ), is available within the MagIC database (http://dx. doi. org/10. 7288/V4/MAGIC/19673). Further descriptions of each study are provided in Text S1 in Supporting Information S1.
A High-Resolution APWP for Cenozoic North America Our new approach propagates uncertainties from the site-level into the final APWP, and selects the optimal value for the width of the moving  (Fisher)  average in a fully data-driven way. Using this approach, we present a 1-Myr resolution APWP for Cenozoic North America (Figure  3 , Figure  S2  and Table  S1  in Supporting Information S1), available in a Github repository associated with this paper (https://github. com/PolarWandering/Gallo_ etal_2023_APWP_construction/blob/main/data/NAM_Cenozoic_APWP. csv). Although our database consists of 1,796 records, we based our analysis on the original authors' filtering of the sites, that is, 952 VGPs derived from 39 studies. Future work considering alternative quality selection criteria could broaden our understanding of their effects (e.
Data Availability Statement All data are included in a Github repository associated with this paper (https://github. com/PolarWandering/Gallo_ etal_2023_APWP_construction) that is also archived in Zenodo (https://doi. org/10. 5281/zenodo. 7860336). This repository provides an accessible implementation of this analysis for community use via Jupyter notebooks  (Kluyver et al. , 2016). Additional analysis was performed using PmagPy  (Tauxe et al. , 2016) , Scipy  (Virtanen et al. , 2020) , and NumPy  (Harris et al. , 2020). Graphics were created using seaborn  (Waskom, 2021)  and Cartopy  (Met Office, 2010). Paleomagnetic data compiled in this study have been made available in the MagIC database (http://dx. doi. org/10. 7288/V4/MAGIC/19673).
The early Palaeozoic biodiversification is the most significant radiation of marine ecosystems of Earth's History, starting with the appearance of invertebrate organisms near the Precambrian-Cambrian boundary and followed by a significant diversification during the Great Ordovician Biodiversification Event (GOBE). The apparently sudden appearance of major phylums of metazoans during the earliest Cambrian (i. e. , the 'Cambrian Explosion', c. a. 540 Ma) has been considered by many as corresponding to an 'explosive' process that took place during a very short time-interval. Similarly, the GOBE has been considered as a short spectacular global event triggering a massive biodiversification during the early Middle Ordovician (c. a. 470 Ma). However, it appears that both 'events' have been more intensively studied in a few locations, creating multiple gaps, and thus a bias, in the biodiversity datasets.
For example, the 'Furongian gap' (late Cambrian) is clearly an artefact in the Paleobiology Database (PBDB), which includes mostly data from North America and Western Europe. These geographic areas were more intensively sampled and recorded in the PBDB than others (e. g. , China), separating the 'Cambrian Explosion' and the GOBE artificially, whereas the Geobiology Database (GBDB), which is focused on data from eastern Asia, record a more gradual increase of the global diversity during the early Paleozoic. These diversity curves are indeed not truly global, but the result of patchy data from different palaeocontinental margins. Moreover, the evolution of global biodiversity is mostly estimated only in two dimensions (taxonomic richness versus time) and spatial distribution is rarely assessed.
The organisms might have occupied the Earth surface heterogeneously because of constraints on their ecological niches, generating 'diversity hotspots' that were recorded in some database and not another one. We want to test whether global 'explosions' of diversity ever occurred, or instead a single, but very complex, long-term evolutionary process took place over space and time, starting in the late Precambrian and lasting throughout most of the early Palaeozoic, with changing 'diversity hotspots' at different paleogeographical locations. To do so, we use the echinoderms as a model and we propose a protocol to (1) to assess the validity diversity curves based on data currently available online, and (2) to analyse the spatio-temporal evolution of their diversity (i. e. , generic richness) in the Cambrian and the Ordovician.
We first compare echinoderms diversity curves between the PBDB, the GBDB and an original database built from a comprehensive synthesis of the literature, with a temporal resolution at the scale of the stratigraphic stage. Then, we gather the three datasets to build a synthetic database that includes a revised taxonomy. To avoid fake spatio-temporal diversity peaks and hotspots, we define comparable stratigraphic units in terms of temporal range and gather sampled sections that refer to the same locality (e. g. , the Montagne Noire). We will present the comparison of the diversity curves between the three databases and the details of the whole methodological protocol.
Acknowledgments L. H. is supported by the  Swiss National Science Foundation  grant  199307 , as well as the European Union's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No 945298 -ParisRegionFP. She  is a  Fellow of Paris Region Fellowship Programme  supported by the  Paris Region , and acknowledges the support of the  COST Action   CA18108. This research has made use of data, software and/or web tools obtained from the  Gravitational Wave Open Science Center  (https://www. gw-openscience. org),a service of  LIGO Laboratory , the  LIGO Scientific Collaboration  and the  Virgo Collaboration. LIGO is funded by the  U. S. National Science Foundation. Virgo is funded by the  French Centre National de Recherche Scientifique (CNRS) , the  Italian Istituto Nazionale della Fisica Nucleare (INFN)  and the  Dutch Nikhef , with contributions by Polish and Hungarian institutes.
The mainshock occurred along the Xianshuihe fault near the eastern margin of the Qinghai-Tibetan Plateau, which is one of the most seismically active faults in China. It caused significant damage to nearby towns  (Qu et al. , 2022) , and triggered numerous landslides in the surrounding regions  (Huang et al. , 2022; Le Breton et al. , 2021; Marc et al. , 2021). In this study, we present a rapid slope damage assessment on three slow-moving rock slopes by measuring the dv/v decrease caused by the 2022 M S 6. 8 Luding earthquake at an hour temporal resolution. In addition, using seismic data recorded by 16 sensors deployed on the rock slope ~370 km away from the epicenter, we obtain a spatial dv/v change before and after the earthquake. Finally, we derive the stress sensitivity of velocity by relating velocity changes to earthquake-induced dynamic stresses.
, 2021) , while the SC is composed of colluvial dolomite rock debris  (Jiang et al. , 2018). We utilize continuous seismic data recorded by a near-real-time slope damage monitoring system on each slope. The system is composed of seismic sensors, data acquisition/transmission units, as well as a cloudserver-based real-time computing and monitoring unit. The data acquisition/transmission units are compatible with conventional ground motion sensors including strong-motion accelerometers, and weak-motion short-period seismometers (velocimeter). We also use a dual output sensor (named landscope) for the purpose of measuring the ambient noise of the slopes. It combines a velocimeter with an accelerometer, which can better record both weak and strong ground motions in such a seismic active environment.
Acknowledgments This manuscript benefits from useful comments by two external reviewers. We thank the managing editor  Ms. Qin Xu  and deputy editor  Prof. Zhigang Peng  for proofreading the manuscript. Fan Xie  acknowledges the support from the  National Science Foundation of China  (Grant No. NSFC41874061 42120104002 ) and the  Central Research Institutes  Fig. 5. (a) The measured PGV at each vertical component of seismic stations on the slope SC due to the mainshock. (b) Stress sensitivity of velocity change.
). In total, 27 reference points were used with uncertainties for position ranging from 4 to 40 m with an average of 18 m. The right and left bank were manually calculated for the 1785 and 1848 paths of the Seine River using the ArcGIS software. To compute the channel migration, we used the approach developed by Lemay et al. 38  , which is based on the dynamic time warping algorithm  36  (the Github repository is available here http:// github. com/ martin-lemay/ Chann elPy. git). This algorithm calculates the similarities based on proximity along curved centrelines to calculate migration distances. We performed analyses on the left and right banks of the Seine River in between 1785 and 1848. Only the left bank is shown on Fig. 4a. The centreline points were resampled every 20 m. The maximum distance for the calculation was set to 240 m, i.
Second, turbidity currents that erode the seabed can increase in density and speed, as more sediment is incorporated into the flow, causing even more erosion and acceleration, producing a positive feedback effect known as ignition 25 ( ). Last, the rate of erosion and deposition of sediment could be balanced, such that turbidity currents maintain a uniform velocity and near equilibrium state termed autosuspension 4, 25 ( ). Direct monitoring measurements can be used to test these hypotheses. Detailed information on the spatial changes in the speed of the flow front is only available for a handful of sites, but these datasets show a remarkably consistent pattern 4,40,87 ( ). Flow behaviour tends to bifurcate, depending on the initial velocity. Initially fast-moving flows (>4-5 m s -1 ) sustain near-uniform front velocities or gradually accelerate, and thus have large runout distances 4,87.
And methods combining neural architectures and differentiable physical models already exists  [6, 17, 12]. Most of the time, a large database is leveraged to learn a regularization adapted to the task. Fitting observations respecting the dynamical model can be seen as a form of regularization but an additional regularizer may be required to promote acceptable solution  [9]. Recently a very original idea called deep prior  [18]  has been developed. A neural architecture is used to generate the solution of an inverse problem and acts like an implicit regularization. Astonishingly the whole architecture is trained in an unsupervised manner on one example and provides results comparable to supervised methods. In this work, we propose a hybrid methodology bridging deep prior and variational data assimilation and we test it in a twin experiment.
Differential stresses also influence the calculations of residual pressures, and determinations of strains from Raman frequency of quartz inclusions have relied on first-principles calculations of Raman peak shifts  (Murri et al. , 2019) , a method that was also successfully used for determining differential stresses in high-pressure experiments  (Reynard et al. , 2019)  but that is backed only by experimental data at liquid helium temperature  (Briggs and Ramdas, 1977; Tekippe et al. , 1973). New experiments were performed to directly calibrate the Raman peak shifts induced by hydrostatic and nonhydrostatic stresses. Consequences for barometric applications are discussed based on available data on synthetic inclusions from piston-cylinder experiments  (Bonazzi et al. , 2019)  and from recent datasets on natural quartz inclusions in different suites of metamorphic rocks  (Cisneros et al.
10 mW. The typical duration for each measurement was 1 to 2 min with > 5 times repetition to obtain good signal-to-noise ratio. Immediately after each inclusion measurement, a gem-quality quartz crystal was measured as stress-free reference material to obtain the wavenumber shift of quartz inclusions due to residual stress. Peak positions were fitted using symmetric Voigt profiles, although some bands, in particular the 206 cm -1 peak, are slightly asymmetrical in shape. This ensures that the present calibration is comparable with data obtained on natural inclusions where a Voigt profile must be used because interference with bands of the host garnet does not allow us to use more complex functions to fit peak positions. Nominal frequencies of the three main quartz peaks of 127. 1(2), 206. 1(2), and 464.
For the 128 cm -1 peak, the two components of the E mode are split by compression perpendicular to the c axis (open circles and arrows). Stress dependence (long dashed line) is the average of that of the two components  (Briggs and Ramdas, 1977; Tekippe et al. , 1973). A few data points were collected at effective stresses higher than 0. 6 GPa in partially broken crystals, and the stress is then not calibrated because the crystal section is not known anymore. These points plot along extrapolation of the linear trends even though they were not included in the fit. via the stiffness tensor. Assuming σ 1 = σ 2 due to symmetry, the Raman shift is related to stress as follows: ν = 2 ∂ν ∂σ 1 σ 3 σ 1 + ∂ν ∂σ 3 σ 1 σ 3 , (4) where ∂ν ∂σ 1 σ 3 and ∂ν ∂σ 3 σ 1 are directly fitted from the experiment (Table  1 ) and assumed constant.
Comparison with natural inclusions We used recent datasets on natural inclusions from three different metamorphic suites: (1) blueschists from Syros in garnet formed at ∼ 1. 4-1. 7 GPa and 500-550 • C and in retrograde epidote grown between ∼ 1. 3-1. 5 GPa and 400-500 • C as well as ∼ 1. 0 GPa and 400 • C  (Cisneros et al. , 2021) ; (2) Holsnøy eclogite from the Bergen Arcs (Norway), where inclusions formed at 1. 4-1. 6 GPa and 680-760 • C, consistent with previous estimates based on phase equilibria  (Zhong et al. , 2019; Bhowany et al. , 2018) ; (3) gneiss from Papua New Guinea with formation conditions estimated from Ti-in-quartz and quartz-in-garnet inclusions as ∼ 1. 0 GPa and 600 • C, which are interpreted as the P-T conditions of garnet growth and entrapment of quartz inclusions  (Gonzalez et al.
High pressure neutron diffraction experiments Polycrystalline sample of metallic natural Ho with high purity (99. 999% ) was purchased from Sigma-Aldrich. Especial care was taken to manipulate the sample minimizing the exposure time to air. Neutron powder diffraction experiments were carried out on the high-flux 2-axis neutron diffractometer D1B of the Institut Laue-Langevin (ILL) in Grenoble, France. This instrument has a MWGC 1D-detector spanning an angular range of 128 • with a definition of 0. 1 •. A Radial Oscillating Collimator (ROC) was installed in order to eliminate the spurious signals produced by the sample environment. Two data acquisitions were taken at AP and room temperature (RT) for a Ho powder sample inside a 6 mm diameter vanadium can, with neutron wavelengths of = 1. 28 Å and 2.
Acknowledgements This work was supported by  Grants-in-Aid for Scientific Research , Grant No. 19KK0070 , from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. The authors acknowledge support by Grants No. PID2022-138492NB-I00-XM4  funded by  MCIN/AEI/10. 13039. 501100011033 ,  OTR02223-SpINS  from  CSIC/MCIN  and  DGA/M4  from  Diputación General de Aragón (Spain). MPS acknowledges a predoctoral research fellowship from  Diputación General de Aragón (Spain). Authors acknowledge the SANE service of ILL and in particular to  C. Payre  for his valuable support during the experiments.
, 2016 (Ackerer et al. , , 2022)). In the second case, catchment-averaged soil thickness is the 'Depth to bedrock' derived from machine learning modelling using a global database of soil profiles  (Hengl et al. , 2017; Shangguan et al. , 2017). Soil thicknesses used in our calculations range from 15 to 21 m, which is greater than what is expected at least in alpine catchments. Thus it is unlikely that soil thickness is under-estimated yielding residence times that are too short. In the third case, it is possible that the measured ( 234 U/ 238 U) activity ratios and/or surface area are too low. Lowering U isotope ratios during sample preparation is unlikely as whether authigenic phase removal was incomplete or grain leaching was too aggressive, this would result in ratios that are too high  (Francke et al.
While this hypothesis cannot be tested with our current dataset, future work should aim at further investigating the factors explaining the observed discrepancy between U isotopes-vs 10 Be-derived residence times. Regardless the source of the discrepancy in absolute values between residence times calculated by two different methods, their correlation indicates that U isotopes in sediments are able to identify the range and variability of sediment residence times, and spatial data on soil thickness combined to in-situ 10 Be measurements are a suitable alternative method.
Sediment residence times sharply decrease where catchment-averaged slopes exceed 30 • , reflecting the transition from soil-covered to bedrock-dominated landscapes. The sediment residence time was also estimated independently using spatial data of soil thickness and 10 Be-derived denudation rates and shows good agreement with that calculated using U isotopes. This test further demonstrates the suitability of using U isotopes to infer sediment residence times. Our results show that despite the Var River basin being a reactive sediment routing system, with little accommodation space for alluvial storage, sediment residence times generally exceed 200 kyr, reflecting long hillslope storage. Uranium isotopes and inferred sediment residence times act as a tracer of geomorphic environments (e. g.
Acknowledgements MT was supported by a joint  IFREMER -University of Wollongong  postgraduate scholarship. This work was supported by  ISblue  project,  Interdisciplinary graduate school  for the blue planet ( ANR-17-EURE-0015 ) and co-funded by a grant from the  French government  under the program " Investissements d'Avenir ". We thank  Korin Brown  and  Alexandru Codilean  for help with GIS analysis, and  Alexander Francke  for help in the laboratory. ST is grateful to  L. Bonneau ,  S. J. Jorry  and  R. Silva Jacinto  ( Ifremer ) for assistance during fieldwork.
ACKNOWLEDGMENTS This work was performed at the Matter  at Extreme Conditions (MEC)  instrument of LCLS, supported by the  U. S. Department of Energy Office of Science, Fusion Energy Science  under Contract No. SF00515. D. K. , A. M. S. , and R. W. F. acknowledge support by the  U. S. Department of Energy, Office of Science, Office of Fusion Energy Sciences , Award No. DE-AC02-05CH11231 , and  National Nuclear Security Administration , Award No. DE-NA0003842. The XFEL experiments on SACLA were performed using BL3 at the EH5 with the approval of the  Japan Synchrotron Radiation Research Institute (JASRI)  (Proposal No. 2019A8070 ). D. K. , N. J. H. , J. L. , A. K. S. , and K. V. were supported by the  Helmholtz Association  under  VH-NG-1141. SLAC HED is supported by  DOE Office of Science, Fusion Energy Science  under  FWP 100182.
To do so, we prepared 3D printed analogues and measured the interaction of a multi-frequency electromagnetic wave within these analogues. These experiments were carried out in the laboratory in an anechoic chamber. The measurements were performed in a quasi-monostatic configuration, that is, a transmitter-receiver pair, which measures the scattered field, was moved close together on a spherical surface centred on the analogue. This data set was jointly processed with two inversion methods: one in the time domain and the other in the frequency domain. This latter is the subject of this paper and Paper I of the joint study. Imaging procedures have been developed by the inverse problems community over the last decade. A review of 3D A72, page 1 of 16 inverse full-wave algorithms tested against the same experimental data set can be found in the special section devoted to inverse problems in 3D Fresnel database  (Geffrin et al.
Acknowledgement. Centre de Calcul Intensif d'Aix-Marseille  is acknowledged for granting access to its high-performance computing resource. The authors acknowledge the opportunity provided by the  Centre Commun de Ressources en Microonde  to use its fully equipped anechoic chamber. L. -I. Sorsa ,  Y. O. Yusuf  and  S. Pursiainen  were supported by  Academy of Finland Centre of Excellence in Inverse Modelling and Imaging  ( 2018-2025 ) and project  336151.
Detection, Location and Magnitude Estimation We pick P-and S wave arrivals of earthquakes on daily raw waveforms using EQTransformer, an automatic deep learning phase picker trained on a worldwide earthquake database  (Mousavi et al. , 2020a). We associate phases picks into events with REAL  (Zhang et al. , 2019a) , performed over a 3° by 3° grid. We only consider events for which both P and S phases are associated on at least three stations. We locate events using NonLinLoc  (Lomax et al. , 2000)  in a 3D velocity model of Chile  (Ruiz et al. , 2017). We discard events with a NonLinLoc root mean square residual (RMS) above 1 s to avoid false detections. We then estimate a local magnitude following the original Richter approach on Wood-Anderson seismometers. For that purpose, we correct the recorded waveforms from their instrument response and convolve them with a Wood-Anderson response.
The 2017 activity is located on the shallowest part of the subduction surface with no direct connection with deeper activities. We compare our ValEqt catalog with the CSN catalog (Figures  1d  and 1f ) in the same sub-region. The Gutenberg-Richter distribution in Figure  1e  shows that the ValEqt catalog includes much more small magnitude earthquakes than the CSN catalog, lowering the local magnitude of completeness from 𝐴𝐴 𝐴𝐴 𝐶𝐶𝐶𝐶𝐶𝐶 𝑐𝑐 = 3 to 𝐴𝐴 𝐴𝐴 𝑉𝑉 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 𝑐𝑐 = 2. Our detection procedure identified almost all earthquakes on CSN catalog. We only miss 12 CSN earthquakes all with a magnitude below three, either because the data of the 13 stations used in our study were unavailable at that time or these earthquakes were interlaced with the waveform of a preceding earthquake, making it difficult to pick their P and S phases even after a careful review.
We can write the G-R probability density function as: 𝑓𝑓0(𝑀𝑀) = 𝛽𝛽e -𝛽𝛽(𝑀𝑀-𝑀𝑀 𝑐𝑐 ) , ( 3 ) β = b ln(10) with b the b-value of the G-R law. The G-R law and the ETAS model are only defined above the magnitude of completeness M c that is supposed to be constant over time. However, in actual seismicity catalogs, we frequently observe temporal variations of M c  (de Arcangelis et al. , 2018; Hainzl, 2016; Kagan, 2004). Such variations of M c are usually attributed to deterioration of the detection performance of low magnitude earthquakes during network maintenance or during period of high seismic activity. The latter is our main concern for the ValEqt catalog since the data availability is quite constant over the studied time-period. When the seismicity rate is high, records of seismic wave of low magnitude earthquakes are likely to be hidden by larger magnitude events.
Acknowledgments Discussion with  Mathilde Radiguet  for HRGPS post-processing was fruitful. This work was supported by the  European Research Council (ERC)  (under the  European Union 's  Horizon 2020 research and innovation program  under Grants  805256  and  865963 ). Y. I. is an  Overseas Research Fellow  of the  Japan Society of the Promotion of Science. We thank editors  Satoshi Ide , an anonymous associate editor,  Marino Protti , and two anonymous reviewers for their comments which helped improve the manuscript.