dataesr/scientific-paragraphs-categorization huggingface dataset:
dip variability in the remaining 195 dip-slip faults and 157 strike-slip faults. The slip-rate harmonization procedure used 970 faults out of the 1109 INT and SCR faults to determine a weighted average slip-rate variation ratio of 0. 51 that was then applied to the remaining 139 faults, including the Gloria Fault (Dataset #02). The 138 MAR faults were excluded from the slip-rate harmonization because of their peculiar tectonic setting, which, differently from the rest of crustal faults, involves exclusively oceanic crust. The weighted average slip-rate variation ratio for these faults is 0. 46. https://doi. org/10. 5194/nhess-2023-118 Preprint. Discussion started: 11 September 2023 c Author(s) 2023. CC BY 4. 0 License.
Despite their global societal importance, the volumes of large-scale volcanic eruptions remain poorly constrained. Here, we integrate seismic reflection and P-wave tomography datasets with computed tomography-derived sedimentological analyses to estimate the volume of the iconic Minoan eruption. Our results reveal a total dense-rock equivalent eruption volume of 34. 5 ± 6. 8 km³, which encompasses 21. 4 ± 3. 6 km³ of tephra fall deposits, 6. 9 ± 2 km³ of ignimbrites, and 6. 1 ± 1. 2 km³ of intra-caldera deposits. 2. 8 ± 1. 5 km³ of the total material consists of lithics. These volume estimates are in agreement with an independent caldera collapse reconstruction (33. 1 ± 1. 2 km³). Our results show that the Plinian phase contributed most to the distal tephra fall, and that the pyroclastic flow volume is significantly smaller than previously assumed.
Quantification of marine tephra fall volume Guided by the described criteria, we identified and measured the thickness of the Plinian layer, the turbidite layer and the co-ignimbrite/ dispersed layer, which together form the tephra deposits of the Minoan eruption. Combined with more distal thickness measurements from the region (Supplementary Data 2), we compiled isopach maps for the Plinian (red in Fig. 1e ) and the co-ignimbrite/dispersed tephra layers (blue in Fig. 1e ; Supplementary Fig. S2 ). The thickness distribution of the Plinian layer (lower subunit) agrees well with the thinning trend seen in more distal sediment cores from the Eastern Mediterranean  11  and results in isopachs with a southeast-oriented deposit lobe. The co-ignimbrite/dispersed layer (upper subunit) follows the trend of thickness constraints from Aegean Islands, mainland Turkey and the Black Sea, defining a northeast-oriented lobe (blue in Fig.
However, neither seismic data nor sediment cores show indications for such a large mass-transport, making this interpretation highly unlikely. In contrast, our reconstruction of the caldera collapse volume of 33. 1 ± 1. 2 km³ (Fig. 5 ) demonstrates an internal consistency between two independent approaches, implying high confidence in our estimates. A similar volume consistency has been noted for the ~8. 9 km³ of submarineemplaced deposits of the 1883 Krakatau eruption and the associated ~9 km³ caldera volume  33. However, our analysis represents a significantly more detailed and error-constrained approach, by combining a unique dataset of measurements over various scales, from microscopic (sub-millimetre resolution of CT-scans) through (multi)meter (core stratigraphy and multi-channel seismic data) to shallow crustal scales (P-wave tomography).
In the case of Santorini, our results reduce the ignimbrite, caldera infill and total volume estimates significantly, highlighting the large uncertainties introduced by not constraining submarine deposit volumes. Empirical analyses suggest a correlation between caldera area and the associated eruption volume  46. The Santorini caldera (~83 km²) is significantly larger than the calderas of Kuwae (~56 km²), Tambora (~37 km²), and Samalas (~29 km²), while volume estimates for their associated caldera-forming eruptions  [42] [43] [44] [45]  are all larger than our revised Minoan eruption estimate. In light of the large uncertainties of volume estimates without seismic datasets and the volume revision for the  Minoan eruption, our analysis indicates that eruption volume estimates mainly based on the caldera area have large uncertainties.
To reduce the impact of beam hardening effects, we used a 1 mm copper filter. After scanning, CT projections were reconstructed for 3-D analysis with the Fraunhofer Volex 10 software. We then relied on version 9 of the Thermo Scientific Avizo suite for image to (i) assess the volume of tephra layers, (ii) resolve down-core porosity and density variations, and (iii) aid identification of eruption phases. For this purpose, we applied the protocols outlined by (ref. 48) and (ref. 49). First, we calculated down-core CT grayscale variations-which primarily capture density differences and thus capture changes in porosity and sediment composition 50 -for 0. 3 cm 3 volumes at 500 µm intervals. Next, the CT density range of reconstructed scans was manually segmented to highlight air-filled pores. We then assigned a single value to this thresholded density range, before calculating down-core pore volumes at 500 µm intervals.
Reflections seismic analysis The seismic database contains 650 km of high-resolution multi-channel reflection seismic profiles collected during research cruise POS538 on board R/V Poseidon in October 2019 (ref. 53). As seismic source, we used a GI-Gun in harmonic mode with a primary (Generator) and a secondary (Injector) volume of 45 in 3 , which was operated with a shot interval of 4 s resulting in a shot-distance of ~6 m. Seismic signals were recorded by a Geometrics GeoEel digital streamer with an active streamer length between 190 m and 250 m. The processing flow included source-receiver geometry corrections, trace-editing, frequency filtering (15-1500 Hz), surface-related multiple elimination, spherical divergence correction, time-variant frequency filtering, prestack time migration, top-muting, and white-noise removal, bandpass frequency filtering, normal move-out correction, stacking, trace-interpolation, and 2D time migration.
The channel spacing of 1. 56 m and the comparably short shot interval yielded a horizontal seismic resolution of 1. 56 m and the seismic source's main frequency of 125 Hz resulted in a vertical resolution of 4-8 m at the seafloor (using the λ/4or λ/2approximation and a seismic velocity of 1. 6 km/s). These datasets were combined with previously collected multi-channel  25  and single-channel seismic data  21  and analysed using the seismic interpretation software suites Kingdom Suite by IHS Markit and Petrel by Schlumberger. We defined the base of the Minoan ignimbrite as described in the main text and used the seafloor reflection as its top. Both horizons were subtracted and the TWT-differences was converted to thickness using seismic velocities of 1. 68 km/s as a minimum estimate and 1. 88 km/s as a maximum.
P-wave tomography The V p -tomography dataset was acquired on board R/V Marcus Langseth in 2015 using 90 ocean-bottom and 65 land seismometers and a 3600 cubic inch airgun array  28. About 14,300 controlled-sound sources covered an area of 120 × 60 km around Santorini. First arrivals of crustal refractions (Pg) were picked on hydrophone, vertical, or a stack of the hydrophone and vertical channels and integrated into a Pg travel time inversion to calculate a V p -tomography model (ref. 28). Finally, the tomography was converted from the depth into the time domain to be integrated with the reflection seismic dataset using the seismic interpretation software Petrel. marine-geo. org/tools/files/31273. High-resolution versions of the CT scans presented in this study can be accessed at https://doi.
Acknowledgements We thank the masters and crews of  RV Poseidon  for their support during research cruises  POS512  and  POS538. We also thank  Schlumberger and IHS Markit  for granting educational licenses to their software. Jonas Preine's contribution was supported by the  German Research Foundation DFG  ( HU698/25 ). Tim Druitt's contribution is part of the laboratory of  Excellence ClerVolc (Laboratory of Excellence ClerVolc Contribution Number 585). Willem van der Bilt `s contribution was supported by a  Starting Grant  ( TMS2021STG01 ) from the  Trond Mohn Stiftelse (TMS). We would like to thank  Ali Bolkar Kiliç , who carried out density and grain size measurements on the sediment cores in the EARTHLAB facility of the  University of Bergen.
ACKNOWLEDGMENTS The French Oceanographic Cruise MAYOBS19 was funded by the  Réseau de Surveillance Volcanologique et Sismologique de Mayotte (REVOSIMA)  consortium. We thank the chief scientists ( E. Rinnert ,  E. Lebas , and F. Paquet), the cruise scientific team, captains, and crews for their invaluable support during the MAYOBS19 cruise. Thanks to  M. Rovere  and  J. Gouriou  for the core analysis at the Ifremer sediment analysis laboratory (Brest, France);  S. Cheron  and  A. Boissier  at Ifremer for XRD-XRF measurements; and  N. Gayet  for help with SEM images. We also thank  Simon George , two anonymous reviewers, and editor  Rob Strachan  for their constructive comments.
We logged all walls at a 1:10 scale. The samples taken for dating include charcoal and bulk material that were sent for dating by radiocarbon methods (C 14 ) at Beta Analytics Testing Laboratory. The results and details of the samples are reported in Table  4. Ages in the Results section are depicted as conventional ages and in the Discussion section they are presented as calibrated at 2 sigma of confidence level using the OxCal 4. 1 program (Bronk Ramsey, 2009). The value for the maximum possible magnitude is calculated by means of the  Wesnousky (2008)  Length-Magnitude empirical relationship. This formula was developed for normal faults in extensional environments based on the surface rupture length value (SRL). This relationship is an appropriate choice for the seismotectonic environment of the Acambay Graben active faults.
Data Availability Statement The Hillshade imagery utilized in Figures  1  and 4a  can be accessed freely from the Instituto Nacional de Estadística y Geografía (INEGI, 2022). The digital elevation model GMRT employed in Figures  2  and 4b  originates from GeoMapApp  (Ryan et al. , 2009). The epicenters displayed in Figure  1  were acquired from the catalog of the Servicio Sismológico Nacional (http://www. ssn. unam. mx/). The slab contours depicted in Figure  1  were derived from the Slab v. 2 dataset  (Hayes, 2018). The polygons representing urban areas in Figure  1  were obtained from CONABIO (2015).
Volcanism in the northern Mozambique Channel from  Michon et al. [2022]  and  Roche and Ringenbach [2022]. Earthquake locations from the database of the International Seismological Centre  [2021] ; plate boundaries from  Michon et al. [2022] ; fracture zones from  Davis et al. [2016] ; boundary of oceanic crust and exhumed mantle (COB) from  Roche and Ringenbach [2022] ; major faults of the East African Rift System (heavy black lines) from  Michon et al. [2022] ; bathymetry from GEBCO, 2014  [Weatherall et al. , 2015]. A, Ambilobe basin; DR, Davie Ridge; K, Kerimbas basin; M, Majumba basin; N, Nacada basin. (b) Focal mechanisms compilation  [Bertil et al. , 2021, and  references therein] and present-day relative plate motions with respect to Nubia plate  [Stamps et al. , 2021]. ter map the distribution of deformation around the Archipelago.
Dataset from SISMAORE cruise and methods The SISMAORE cruise (December 2020 to February 2021) of R/V (Research Vessel) Pourquoi Pas? collected 80,000 km 2 of multibeam bathymetry and backscatter data around the Comoros Archipelago with the vessel-mounted MultiBeam Echosounder System (MBES RESON 7150 at 12 or 24 kHz) that complemented previous surveys around Mayotte [Figure  2 ;  Thinon et al. , 2020b;  for details, see Supplementary Information]. In addition, the ship acquired 10,000 km of sub-bottom profiler (SBP) data and 6730 km of 48-channel seismic reflection profiles (for details, see Supplementary Information), imaging the subsurface down to 0. 1 s two-way travel time (TWT) with very high resolution and down to ∼3 s TWT below the seafloor, respectively. Five dredging operations (Figures  2  and 3 ) collected mainly volcanic rocks on the flanks of Zélée Bank (sample set SMR1), the Jumelles volcanic chain (SMR2), Mohéli (SMR3), the Chistwani volcanic chain (SMR4) and a seamount in the abyssal plain north of Mayotte (SMR5)  [Thinon et al.
The distribution of volcanic and tectonic structures, and by inference the orientation of major tensile fractures, shifts in segments from a roughly N-S orientation in the west, to N160°E, to N130°E to E-W in the east. The Comoro Islands tend to lie at the junctions between tensile fractures (N160°E and N130°E) and ENE-WSW lineaments. This regional distribution of the recent structures is consistent with seismicity patterns in the Comoros Archipelago. We interpret the Comoros tectono-volcanic corridor as a marker of the Somalia-Lwandle plate boundary. Although the corridor is prone to episodes of intense volcanism, the low intensity and style of deformation tends to confirm the very immature state of this dextral strike-slip plate boundary. analysis of these geophysical and geological data are carried out mainly in the framework of the COYOTES project (ANR-19-CE31-0018; https://anr.
Acknowledgments The SISMAORE cruise was mainly funded by the  Flotte Océanographique Française  and the  French geological survey (BRGM). We thank  Captains P. Moimeaux  and  G. Ferrand  of R/V Pourquoi Pas? and its crews and technicians especially and GENAVIR team for the bathymetry processing and the seismic deployment. Thanks to the  BRGM regional department of Mayotte, Ifremer, REVOSIMA  and the Délégation interministérielle aux risques majeurs Outremer for their assistance during the COVID health crisis. We also thank the  Rectorate of Mayotte  and two of its teachers ( J. Jacquot ,  P. Deparis ) for their participation, the Mission Sismaoré blog (https://sismaore. ac-mayotte. fr/) and the primary and high school students for the richness of exchanges. Thanks to the laboratory of  BRGM  for gas analysis and to  D.
The particular choice of these boreal seasons in contrast to the local ones is supported by previous research showing that this coast is strongly influenced by the swells that develop at high latitudes, both north and south. In order to understand the causes of the observed variability, the SAM index was obtained from the British Antarctic Survey (BAS, www. nerc-bas. ac. uk/icd/gjma/sam. html, accessed on 1 February 2023) (Marshall et al. [34] ). The North Atlantic Oscillation Index (station-based), proposed by Hurrel and made available through the NCAR National Center for Atmospheric Research (NCAR), can be accessed at (https://climatedataguide. ucar. edu/climate-data/hurrellnorth-atlantic-oscillation-nao-index-station-based, accessed on 1 February 2023). Other Northern Hemispheric Teleconnection Indices were investigated (East Atlantic and Scandinavian modes) with data provided by Comas-Bru and Hernández  [67] , derived from instrumental data (https://doi.
org/10. 1594/PANGAEA. 892768, accessed on 1 March 2023). These climate indices were selected because the associated climatic modes are known to explain the main part of the atmospheric variability in the Atlantic Ocean. Indeed, NAO, EA, and SCAN are the leading modes of climate variability for the North Atlantic Ocean, and they can be recovered from rotated principal component analysis of sea-level atmospheric pressure fields  [40]. SAM is the leading mode of climate variability in the South Atlantic Ocean and corresponds to the leading empirical orthogonal function (EOF) in many atmospheric fields, including surface pressure, geopotential height, surface temperature, and zonal wind  [34]. SAM is characterized by a large-scale alternation of atmospheric mass between the mid-and high-latitudes associated with a meridional shift in the atmospheric westerly winds  [15, 34]  at latitudes where the swells reaching Senegal are formed.
Limitations of the Present Model The wave climatology of the Senegalese Coast was analyzed based on a new wave hindcast from 1980 to 2021. This relies on the WW3 model implemented with high spatial (0. 05°) and temporal (1 h) resolutions and state-of-the-art parameterizations for wind input and dissipation by whitecapping. Model/data comparison showed that the present configuration was able to reproduce well-defined wave parameters (Hs, Tm02, and Tp) with an accuracy comparable to previously published studies using high-resolution spectral wave modeling (e. g. ,  [18, 61, 68] ). The present hindcasts showed a substantial improvement compared to available wave hindcasts such as ERA5, which suggests that global hindcasts are too coarse to investigate wave climates along the coasts of Senegal.
Limitations of the Present Model The wave climatology of the Senegalese Coast was analyzed based on a new wave hindcast from 1980 to 2021. This relies on the WW3 model implemented with high spatial (0. 05 • ) and temporal (1 h) resolutions and state-of-the-art parameterizations for wind input and dissipation by whitecapping. Model/data comparison showed that the present configuration was able to reproduce well-defined wave parameters (Hs, T m02 , and T p ) with an accuracy comparable to previously published studies using high-resolution spectral wave modeling (e. g. ,  [18, 61, 68] ). The present hindcasts showed a substantial improvement compared to available wave hindcasts such as ERA5, which suggests that global hindcasts are too coarse to investigate wave climates along the coasts of Senegal.
Acknowledgments: This work stems from the scientific collaboration between  Cheikh Anta Diop University (Dakar, Senegal)  and  La Rochelle Université (La Rochelle, France). This study would not have been possible without the availability of the ERA5 wind field reanalysis, supported by the  European Centre for Medium-Range Weather Forecasts (ECMWF) , and the WWIII model provided by the  US NOAA. We would like to thank the various teams of the  Laboratoire Mixte International d'Etude du Climat d'Afrique de l'Ouest (ECLAIRS)  for sharing the MELAX buoy-observatory data off Dakar. Many thanks to the  Centre de Suivi Ecologique de Dakar  and the  Fond Français pour l'Environnement Mondiale (FFEM)  as part of the  West Africa Coastal Area (WACA)   Program  for finding this research (Agreement N •  CZZ2221.
| Data set composition The data set is composed of 61 specimens (46 Mysticeti and 15 Odontoceti) housed in international museum collections at different ontogenetic stages, from embryos (<2-month post-conception) to adults. The baleen whale sample covers the entire diversity of the clade, with at least two specimens representing five of the six living genera: Balaena (bowhead whales, Balaenidae), Balaenoptera (rorqual whales such as minke whales and the blue whale, Balaenopteridae), Caperea (pygmy right whales, Neobalaenidae), Eschrichtius (gray whales, Balaenopteridae), and Megaptera (humpback whale, Balaenopteridae). The toothed whales were used as an outgroup in the phylogenetic comparative methods. The outgroup is composed of five specimens of three genera representing the diversity of odontocetes (Kogia-pygmy sperm whales, Physeteridae; Phocoena-porpoises, Phocoenidae; Lagenorhynchus-spinner and spotted dolphins, Delphinidae).
borealis, B. musculus, and B. physalus). Specimens were binned into four growth stages: early fetus (from embryonic stages to 50% of gestation time), late fetus and neonate (from 50% gestation time to birth, including the first year of life), juvenile (from the second year of life to sexual maturity), and adult (after sexual maturity). This was done to ensure that minor errors in the assessment of the age of the specimens would not undermine the results, as, for most species, basic information such as the length at birth is unknown or contrasting information have been published in the literature  (Berta et al. , 2016; Frazer & Huggett, 1973; Lanzetti et al. , 2020; Rice, 1983). The approximate age of the specimens was determined based on total length. This was either extracted from the literature or collection metadata or measured directly for specimens in fluid collections.
| Shape data acquisition and rendering Skulls for disarticulated osteological specimens and fluidpreserved samples were digitized using medical-grade and high-resolution x-ray computed tomography (CT) and diffusible iodine-based contrast-enhanced x-ray CT (diceCT;  Lanzetti & Ekdale, 2021)  in local institutions. Whole osteological specimens were digitized using a hand-held surface scanner (Creaform GoSCAN!) or photogrammetry, depending on availability. Digitization mode is listed for each specimen in Supporting Information: Table  S1. Parameters and instruments used for CT scanning are listed in Supporting Information: Table  S2 , along with the resolution of each scan and source of the data if it was not scanned primarily for this project. Light surface scans were performed at 0. 2-0. 5 mm resolution depending on the size of the specimen.
All CT data were first imported in ImageJ  (Schneider et al. , 2012)  to be cropped and adjusted for brightness/ contrast where needed. Larger high-resolution stacks were scaled down by a factor of 2 in all three dimensions (i. e. , binning 2 × 2 × 2), reducing the image size and limiting the number of slices to a maximum of 1500 to aid with segmentation. Images were then exported as 16bit TIFF images stacks for segmentation in Avizo 2020. 3 (Thermo Fisher Scientific Inc. ). Skulls of disarticulated osteological specimens were digitally reconstructed by segmenting bone elements. The manually segmented skulls were exported as OBJ mesh files for further processing. For light surface scans, the dorsal and ventral surfaces were digitized separately merged in VXElements 8. 1 (Creaform Inc. ) and then exported as a single PLY mesh file.
Of the 45 Mysticeti specimens, the focus of this study, 35 out of 46 had at least one fixed landmark or curve identified as "missing. " Of these, 21 had "absent" structures, which in 19 specimens was the interparietal, as this bone is not visible in most baleen whale species after birth  (Nakamura et al. , 2016). Therefore, only 14 specimens of the samples had genuinely damaged features that were estimated before performing further analyses. The "fixLMtps" function from the R package "Morpho" v. 2. 9  (Schlager, 2017)  was used to estimate missing landmarks by mapping weighted averages onto the missing specimen from three similar, complete configurations. Estimated landmarks are then added to each deficient configuration  (Schlager, 2017). The deformation is performed by a thin-plate-spline interpolation calculated using the available landmarks  (Bookstein, 1991; Schlager, 2017).
| Data analysis The curves were resampled to set the same number of semilandmark points for each in all specimens using the package "SURGE"  (Felice, 2021) , and slid on the surface to minimize bending energy  (Bardua et al. , 2019). After resampling the curve semilandmark, the final data set used for analysis is composed of 462 points, of which 64 are fixed landmarks and 398 are semilandmarks. Procrustes superimposition (GPA) was performed using the "gpagen" function in "geomorph" v. 4. 0. 1  (Baken et al. , 2021). This allows all specimens to be aligned and scaled, and to extract centroid size (CS) for each configuration, which will be used as a measure of skull size in analyses of allometry. Age and taxonomic information were imported separately to be used as covariates. Graphics of plots were improved using the "ggplot2" package v.
We further applied k-means clustering directly on the shape coordinate data as implemented in the "LloydShapes" function from the "Anthropometry" package  (Vinué, 2017). The clustering analysis was performed twice, once on the entire data set and once on a separate GPA alignment of Mysticeti. The value of k was set to 10 for the entire data set and five for the Mysticeti-only analysis based on the results of the initial PD clustering.