dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank the  Spanish MINECO  for funding support from  AYA2016-75066-C2-1/2-P , and the  ERC  under  ERC-2013 -SyG, G. A. 610256  NANOCOSMOS. J. M. acknowledges the support of  ERC-2015-STG  No. 679852 RADFEEDBACK. S. P. T. M. acknowledges to the  European Union 's  Horizon 2020 research and innovation program  for funding support given under grant agreement No  639459  (PROMISE). R. M. D. acknowledges support provided by an award from the  Simons Foundation  (SCOL# 321183 , KO). G. M. C. acknowledges funding support  fromAYA2017-85322-R. M. T. acknowledges partial support from project  AYA2016-79006-P.
Sample analysis The recovered samples were separated from CsCl by dissolution in water, polished and observed using a scanning electron microscope with acceleration voltages of 5-20 kV. Bridgmanite and ferropericlase grains were distinguished by the brightness contrast in backscattered electron (BSE) images (Fig. 1 ). The volume fraction of ferropericlase was obtained from the BSE images. The area of each bridgmanite grain was determined using an image processing software (ImageJ). The grain size (d) of each grain was obtained from the diameter of the area-equivalent circle. The grain size in log units (log(d)) showed a Gaussian distribution (Fig. 1 ); therefore, the mean grain sizes (d ) were calculated from the mean log(d) based on the Gaussian distribution  37. The bridgmanite and ferropericlase grains were homogeneously distributed in the post-spinel, opx-bridgmanite and glass samples.
More than 130 bridgmanite grains were analysed for each sample (Extended Data Table  2 ). In the sol-gel samples, BSE images were taken on locally homogenous areas. Each data point of the sol-gel samples (Fig. 3a ) represents the grain size and X fpc in an individual BSE image. As mentioned above, the heterogeneity had occurred during the sample synthesis procedure, after which the grains already reached an equilibrated texture (120° triple junction, Extended Data Fig. 1c ). Therefore, the grain growth in each locally homogenous area during the annealing experiment should not be affected. This is confirmed by the consistent results obtained in the sol-gel, glass, opx-bridgmanite and post-spinel samples. Some metallic iron particles that locally appeared in the solgel samples (Supplementary Figs.
49-53) are also expected to have a negligible effect on the log(d)-X fpc relation because of its small volume fraction in comparison with ferropericlase. The mean interparticle spacing (r ρ = 1/ 1/2 ) was calculated from the two-dimensional density of ferropericlase (where ρ is the number of ferropericlase particles per μm 2 ). Note that r becomes invalid for X fpc = 0% and becomes inappropriate for the high-X fpc samples (greater than about 30%) in which ferropericlase grains are significantly or completely interconnected (Extended Data Table  2 ). The chemical compositions of bridgmanite after grain growth were analysed using an electron probe microanalyser (EPMA). An acceleration voltage of 15 kV and a beam current of 5 nA were used. The counting time was 20 s for each point analysis. An enstatite crystal and metallic iron were used as standards for Mg, Si and for Fe, respectively.
During a transect, the FCTD profiled over ∼100 m vertical range and to within < 10 m of the seafloor. Data from the FCTD were monitored and used interactively to guide the survey. The micro-conductivity probe was used to estimate dissipation rate of temperature variance (χ). Conductivity gradients can be used since temperature dominates the conductivity variance 3. varied in time or along the length of the patch. During the first cross-canyon transect, the dye was spread fairly uniformly across the canyon width; however, in the second transect, the dye was banked on the northeast side of the canyon. Thus, along-canyon transects, which focused on the canyon axis, may at times have sampled through only the edge of the patch. With only the velocity measurements from MP1 (down-canyon of the dye for most of the experiment), it is difficult to estimate where in the cross-canyon direction the dye patch might have been.
Geophysical measurements such as gravity (L. Gailler et al. , 2009; Malengreau et al. , 1999) , magnetism (L. -S. Gailler & Lénat, 2012; Galdéano et al. , 1988) , magnetotelluric (L. -S. Gailler et al. , 2018) , and deformation (e. g. , Q. Dumont et al. , 2022; Froger et al. , 2015; Fukushima et al. , 2010; Peltier et al. , 2005 Peltier et al. , , 2009;; Smittarello et al. , 2019 ) also provide strong constraints on the structure and dynamics of the volcano. In this paper, we focus on electrical resistivity and magnetism measurements carried out to image the consequences of the eruptive dynamics on the edifice structure at depth. This can be used to help provide constraints on the main paths of weakness at depth that could be associated with potential future magma transfer. Here, we present data sets acquired during three magnetic measurement surveys that were carried out along a profile across the N-NE flank of the Piton de la Fournaise volcano between 2017 and 2020.
Magnetic Measurements and Data Treatment Magnetic measurements were acquired during three field surveys in May 2017 (2017a, b, and c), August-September 2019, and November 2020, along a 4-km-long E-W profile (Figures  1  and 3a ). The three magnetic surveys were performed using the same portable Overhauser proton-precession magnetometer (GEM GMS19) in walking mode (0. 5 s sampling interval), with a sensor mounted on a backpack at a height of about 1. 80 m above the ground. The absolute instrumental accuracy was ±0. 1 nT. The integrated GPS enables the position to be determined simultaneously while surveying, with a fairly precise (i. e. , around 1 m) location of the magnetic measurements even without any postprocessing. The first survey was performed in May 2017. Measurements were repeated 3 times during this first survey in order to (a) estimate the quality of the measurements and (b) record any variations in the magnetic signal due to magmatic activity at depth.
This assumption was confirmed during our surveys, since the difference between the magnetic field values for collocated points in the stable area, during each survey and between them, was less than 50 nT on average. Therefore, no correction was made for the diurnal variation. Magnetic signals are extremely noisy at low elevation above highly active volcanic terrains such as this, with recent lava flows typically exhibiting high-amplitude short-wavelength signals (L. Gailler & Kauahikaua, 2017). This can be explained by their high magnetization, their irregularities at a scale of meters or tens of meters, and the rugged flow surfaces. We also systematically checked on the Intermagnet site to verify that no magnetic storms occurred during the measurement periods. Before each survey, the obvious spikes in the magnetic field data (ranging from about 38,000 to 46,000 nT) were removed manually by a careful visual examination of the overall data sets, on a year by year basis.
The high-frequency and short-wavelength signals were then filtered using a spline function, allowing the smoothness and tension to be controlled in the same way for each data set. The best result was obtained with a smoothness and tension of 0. 6, similar to a low pass filter at 10-15 m. Any site effect is therefore intrinsically filtered, effectively removing any bias from the distribution and values of the magnetic signal in the profile, and enabling a qualitative analysis to be performed. The two first sets of the 2017 measurements (2017a and b) were acquired 3 days apart, 3 days before the May 2017 intrusion. They showed a stable magnetic signal, with variations of less than 10 nT on average along the overall profile. On the contrary, a strong decrease of more than 1,000 nT was recorded after the intrusive event (2017c) along the N120 RZ-NERZ area just below the western part of the profile, while the signal remained unchanged especially in the central part of the profile (Figure  3b ).
2D Electrical Resistivity Tomography A long ERT profile was acquired simultaneously with the first magnetic data set (Figure  5a ). The measurements were performed in May 2017 using an ABEM SAS4000 resistivity meter with 64 electrodes along a 2,520-m-long cable, with the electrodes spaced at 40 m intervals. The total length of the profile was 3,780 m, so the full length was obtained by using one full acquisition (2,520 m long) followed by two roll-alongs each made up of a quarter of the total cable length. The field protocols were the same as those described by  Revil et al. (2011). A poledipole configuration was used to increase the depth of investigation  (Pucci et al. , 2016) , down to a depth of about 800 m below the surface in its central part. It highlighted the following features: (a) a clear transition from East to West in the summit zone with an increase in resistivity eastward, and (b) a strong influence of the hydrothermal system under the western part of the profile, as also suggested by simultaneous Self-Potential measurements  (Barde-Cabusson et al.
Magnetic Modeling Approach For each magnetic survey, we calculated 2D direct magnetic models using GMSYS-2D software (© Geosoft) along the profile length. We did this by computing 2D 1/2 models for which the 2D structures were truncated at the same distance in both directions perpendicular to the 2D section. This distance was set to 1 km, which corresponds to the average extent of the main structures observed at the surface along the survey profile. The magnetic model response is based on the method of  Talwani and Heirtzler (1964)  using the algorithms described in  Won and Bevis (1987). Structural units with assigned magnetization values were defined in the direction perpendicular to the profile. Since magnetic models are nonunique, we tested several initial constraints. As mentioned above, the first assumption for our starting model was based on the resistivity distribution derived from the ERT profile (Figure  5a ).
Discussion and Conclusions Magnetic measurements were carried out over three periods between May 2017 and November 2020 along a reference profile located to the north of the Central Cone at the Piton de la Fournaise. Exactly the same configuration was used for all the surveys (same operator and instrument assembly, sensor at 1. 80 m above the ground, sampling frequency at 2 Hz). The data were all reduced using the IGRF, then smoothed and intrinsically filtered from any site effect lower than 10-15 m in order to perform a temporal qualitative analysis. Significant variations (i. e. , up to 2,500 nT) were noted in the magnetic signals along the N120 RZ, and even more strikingly along the GAILLER AL. 10. 1029/2022JB025290 11 of 14 eastern flank. We reduced the main negative signals to the pole in order to isolate those demagnetization areas that might be linked to volcano dynamics.
Acknowledgments The ERT survey and the first magnetic measurements were funded by The  French National Agency for Research  ( ANR  contract  16-CE04-0004-01 SlideVOLC ). The follow-up magnetic reiteration surveys and the presented study were funded by the  Centre National d'Études Spatiales (CNES) , by the  Laboratory of Excellence ClerVolc  and the  ANR Scan4Volc. We are grateful to the staff of the  Observatoire Volcanologique du Piton de la Fournaise  from  Institut de Physique du Globe de Paris (OVPF-IPGP ) and  Raphaël Antoine (Cerema)  for their support throughout the field work and for fruitful discussions. We also warmly acknowledge the  Parc National de la Réunion and InAirTech. The editors,  Douglas Schmitt  and  Agnes Kontny , and the two reviewers,  Bill Moris  and an anonymous one, are acknowledged for their constructive comments and annotations that improved the quality of the manuscript.
Data Availability Statement The data presented in this study can be found online: https://doi. org/10. 5281/zenodo. 6938137 (L. Gailler, Labazuy, Dumont, et al. , 2022). InSAR data were acquired within the framework of the Indian Ocean InSAR Observatory (OI2/OPGC/SNOV/INSU), where the interferograms used in this study are available on the CASOAR link (https://wwwobs. univ-bpclermont. fr/casoar). Licensed commercial software has been used for geophysical data modeling and visualization: © Res2DInv (Aarhus Geosoftware), © Oasis Montaj (Geosoft), and © Golden Surfer. InSAR data inversions were performed on the supercomputer facilities of the Mésocentre Clermont-Auvergne.
(2022)  at near-field. Here, we strengthen the study with a spatial pattern analysis and expand the investigated dataset more globally (Pacific-wide). We seek to isolate the ionospheric signature of the tsunami from the acoustic and Lamb signals. Because of these multiple, partially overlapping signals, we don't expect the discrimination to be straightforward, yet, it is a necessary step to assess the potential of TEC data for tsunami earlywarning even in the case of a volcanic eruption. To support our TEC signal analysis, we first analyze the ionospheric imprint of a tsunami initiated by the Mw 8. 1 Kermadec earthquake, which occurred a year before, on March 4th, 2021 about 1000 km South of Tonga (29. 723°S 177. 279°W, based on the USGS report) (Fig. 1a ). Both events occurred in the Eastern region of Polynesia islands sparsely equipped with GNSS stations installed onland.
Introduction The supplementary material consists of Text S1 & S2, Table  S3  and Figures  S4 -S15. All GNSS data are freely available from the Geoscience Australia data archives (ftp://ftp. data. gnss. ga. gov. au/daily/) and the CDDIS data archives (https://cddis. nasa. gov/Data_and_Derived_Products/GNSS/daily_30second_data. html). The ocean bathymetry data ETOPO1 (1-minute global relief model;  Amante and Eakins 2009)  and the open-ocean sea-level measurements (DART) are from the NOAA data archives (https://www. ngdc. noaa. gov/mgg/bathymetry/relief. html; https://www. ngdc. noaa. gov/hazard/DARTData. shtml). The coastal sea-level measurements (tide gauge) are publicly available via the Intergovernmental Oceanographic Commission of UNESCO (http://www. ioc-sealevelmonitor-ing. org/). To generate the tsunami travel times, we take advantage of Geoware TTT SDK software  (Wessel, 2009).
Acknowledgement This work was supported by  French Agence Nationale de la Recherche (ANR)  under reference  ANR-19-CE04-0003  and  Centre national d'études spatiales (CNES)  for  APR  project  UVTECGEOX. We thank  E. Astafyeva ,  P. Coïsson ,  B. Maletckii ,  F. Manta ,  D. Mikesell & M. Ravanelli  for fruitful discussions within an ad-hoc Geoazur-IPGP-NGI working group on the 2022 Hunga volcano eruption.
Data and Resources All GNSS data are freely available from the Geoscience Australia data archives (ftp://ftp. data. gnss. ga. gov. au/daily/) and the CDDIS data archives (https://cddis. nasa. gov/Data_and_Derived_Products/GNSS/daily_30second_data. html). The ocean bathymetry data ETOPO1 (1-minute global relief model;  Amante and Eakins 2009)  and the openocean sea-level measurements (DART) are from the NOAA data archives (https://www. ngdc. noaa. gov/mgg/bathymetry/relief. html; https://www. ngdc. noaa. gov/hazard/DARTData. shtml). The coastal sea-level measurements (tide gauge) are publicly available via the Intergovernmental Oceanographic Commission of UNESCO (http://www. ioc-sealevelmonitor-ing. org/). To generate the tsunami travel times, we take advantage of Geoware TTT SDK software  (Wessel, 2009).
Background In preparation for future missions, in this study, we model the proposed scenarios and perform inversions to investigate the degree to which we can recover magnetized structure. We use the open-source SimPEG package in Python  (Cockett et al. 2015) , and an example code is available online. First, we simulate magnetic field data collected by a helicopter above a given magnetization model; this is the forward problem. From Gauss's law, the relation between magnetization per unit volume M in Am -1 and magnetic field B in teslas is ( ) • ( ) ò m p =   B M r r dV 4 1 , 1 V 0 where r is the radial distance between the measurement point and the magnetic source of volume V, and μ 0 is the magnetic permeability of free space. Using simulated data, d, we then aim to recover our model, m, by solving an inverse problem.
2020a; Ojha & Mittelholz 2023) , and in comparison with orbital spectrometer data, composition has been shown to correlate with higher magnetic fields  (AlHantoobi et al. 2021). Lastly, information on physical properties at depth could be obtained with a gravimeter. Because gravity informs on density and thus the compositional constraints of rock, combined magnetic-gravity surveys are particularly informative in addressing the origin of magnetization. Also, because both fields fall off differently with distance to the source, they have different depth sensitivities. Joint modeling techniques for the combination of petrophysical/geological and geophysical data have been shown to more accurately represent physical properties and could provide further modeling strategies that can make use of combined data sets (e.
Acknowledgments We acknowledge support from  Harvard's Daly Postdoctoral Fellowship  (A. M. ), the  Natural Sciences and Engineering Research Council of Canada  (C. L. J. ), and  CNES  in the frame of the InSight mission (B. L. ). A portion of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). We also thank the open-source community for their ongoing contributions to SimPEG. The software is available on GitHub: https://github. com /AnnaMittelholz/Helicopter.
ESEM observations were conducted in High and Low Vacuum (LV: 1 and 0. 5 Torr). Scanning electron microscope observations were performed under High Vacuum with an accelerating voltage from 15 to 25 keV both for imaging and elemental analyses. Raman spectroscopy was performed (at the CBM-CNRS, France) using a WITec Alpha500 RA spectrometer with a frequency doubled Nd:YAG green laser (wavelength 532 nm). The system is equipped with a motorized displacement stage permitting to scan the sample and to obtain hyperspectral dataset. Data processing then enables to create concentration and composition maps, or to extract the average spectrum of a specific phase  (Foucher et al. , 2017). For this study, hyperspectral data were acquired using a Nikon E Plan 20 Â objective (NA = 0. 40) and a laser power of 13 mW at surface.
Acknowledgements Most of the material was sampled by AF together with  Kathleen Histon  and  Hans Peter Schönlaub , which are acknowledged for their long discussions in the field, assistance and support. Reviewers  Stephen Kershaw  and  Carlo Corradini  provided insightful comments and constructive criticism that helped clarify many of our arguments. We greatly acknowledge useful observations on the manuscript by Editor-in-Chief  Gilles Escarguel  and Invited-Editor  Thomas Servais. We express as well our deep thanks to  Massimo Tonelli  and  Mauro Zapparoli  ( Scientific Instruments Facility, University of Modena and Reggio Emilia ) for their technical support. This paper is a contribution to  IGCP Project 735 ' ' Rocks and the Rise of Ordovician Life ".
1  Phylogenetic tree topology used in comparative analyses for fissipeds and pinnipeds. The tree topology and branch lengths are taken from ref. 48. The swimming styles of crown pinnipeds are established following ref. 30. All illustration are from PhyloPic (www. phylopic. org). statistically significant for many of them (Supplementary Data 6-9) in all count procedures. In general, the phylogenetic analyses of integration show an evenly integrated vertebral column with just subtle differences between regions (Figs. 2c, d  and S1c, d ). Likewise, it should be noted that the p values of the differences between Z-scores from the 2B-PLS analysis indicated the absence of modules across the column of otariids (Tables  1 2 3 ). Assessment of relative development and distribution of axial muscles.
However, our limited CTdataset could represent interspecific differences instead of differences between phocids and otariids. The slightly integrated "thoracic" segment with de-integrated lumbar and cervical regions that characterizes the pattern exhibited by phocids may be related to their locomotor style. Phocids are hindlimb-dominated swimmers that use pelvic oscillation to generate forward thrust, a behavior in which the anterior body is held rigid and the lumbar region is subject to lateral undulations coupled with lateral sweeps of the hind flippers  [25] [26] [27] [28]. Furthermore, their thoracic region is rigid, while their lumbar area is very flexible with large epaxial muscles providing the necessary movement to the lumbar region  31  with a massive and large sacrum  22. Indeed, our CT-scan data support this, as the direction and orientation of the epaxial and hypaxial bundles along the lumbar series are similar in the three specimens (P.
The x, y, z landmark coordinates (Supplementary Data 1) were uploaded to R environment using the geomorph (v. 4. 0. 5) package  46. Subsequently, a phylogeny of the species included in this sample (Fig. 1 ) was assembled using ape package  47. This phylogeny was built following the one published by Nyakatura and Bininda-Edmons  48. We performed a Procrustes superimposition  49  for each vertebral position accounting for bilateral symmetry using the geomorph (v. 4. 0. 5) package  46. We averaged the resulting Procrustes coordinates of each vertebra for those species represented by more than one individual. Afterwards, we performed a phylogenetic Procrustes ANOVA (PGLS  50  ) with log-transformed centroid size as independent variable to test for allometric shape variation using the geomorph (v.
4. 0. 5) package  46. If allometry was significant, we used the residuals as size-free shape coordinates for all subsequent analyses. All these analyses were repeated for the four procedures described above. We carried out the analyses of integration for each count. The strength of integration between each pair of vertebrae was estimated using the shape covariation computed by phylogenetic and non-phylogenetic two-block Partial Least Squares (2B-PLS) and the Z-score 51 using geomorph (v. 4. 0. 5)  46. The Z-score was used as a measure for the extent of integration between pairs of vertebrae  51. To compare the integration strength (Z-score) obtained for pinnipeds in this study with that obtained by Martín-Serra et al. 16  for fissipeds, we repeated the analyses of integration after removing the landmarks digitized on the transverse processes of lumbar vertebrae (L16, L17, L33, L34) and on the ventral part of the centrums (L35-L40).
Changes in the p value obtained from such comparisons, transitioning from non-significant to significant, confirms the boundary location. Note that, using this procedure, there is not a single threshold in the differences values that marks the level of significance, as it can be different comparisons  16. All statistical analyses were performed for three different grouping of the data: all the specimens, only phocids, and only otariids. We were not able to perform the same analysis for odobenids because there is only one living species. To explore whether the patterns of axial integration and modularity relate to the distribution and development of hypaxial and epaxial muscles in pinnipeds, we used CT scans (see Supplementary Data 19 for parameters of data acquisition) of captive animals from the Oceanographic Park (Valencia, Spain) and from the Zooparc de Beauval in France (ZPB).