dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We thank the  Bureau de Recherches Géologiques et Minières (BRGM) , especially  B. Meire , for providing the geological maps, and  B. Hoyez  for his help in understanding the Chalk stratigraphy of the  Seine Valley. We are also grateful to  P. Rabelle  ( Comité Régional de Spéléologie de Normandie ) and  J. C. Staigre  ( Centre Normand d'Etude du Karst ) for providing data on the cave systems. A. R. Farrant and  M. Woods  publish with the approval of the Executive Director, the  British Geological Survey. We appreciate the effort of three anonymous reviewers, which revisions allowed us to improve the work.
Data analysis Larval counts were standardized to number of individuals per 100 m 3 to calculate the relative abundance (RA) of each taxon. The frequency of occurrence (FO) was also calculated. A potential temperature-salinity (T-S) diagram was built for the studied area on Ocean Data View  (Schlitzer, 2009). Water masses were determined based on thermohaline indexes in  Miranda (1985) ,  Castro and Miranda (1998) ,  Piola et al. (2000) , and  Möller et al. (2008). Temperature and salinity obtained with the thermosalinograph were used as proxies to detect fronts in the study area  (Chaigneau and Morrow, 2002). Chlorophyll-a and nutrient data were also examined to detect responses to the fronts and some profiles are presented. In addition to in situ data, we used 8-days satellite images of sea surface chlorophyll-a concentration for the dates of each leg of the oceanographic cruises, with a 4 km spatial resolution from the MODIS Aqua sensor.
Acknowledgments This research was funded by The  Brazilian Council for Science and Technological Development (CNPq)  and the  Brazilian Ministry of Science and Technology (MCT). M. C. Brandão received a PhD scholarship from the  Coordination for the Improvement of Higher Education Personnel (CAPES). A. S. Freire benefited from  CNPq  grants ( 312644/2013-2  and  311994/2016-4 ). The authors thank the  Brazilian Navy  and all crew from the  R. V. Cruzeiro do Sul  for their logistical support during fieldwork, the researches that helped conducting the field work and analysis in the laboratory, and  A. Ciotti  and  R. Pollery , who provided chlorophyll-a and nutrient data, respectively.
, 2006; Behm et al. , 2007a; Brückl et al. , 2007 Brückl et al. , , 2010) )  and local earthquake tomography  (Diehl, 2008)  targeting the crust, the velocity structure of the Eastern Alps is still not fully understood. This is due to the low level of seismicity in the region and insufficient local earthquake data to perform traditional tomographic studies, as well as to the limits of active seismic experiments covering the region. Therefore, ambient noise tomography appears to be perfectly suited to study crustal structure in this area. Prior to this study, parts of the Alps and their surroundings were seismically imaged with noise-based tomography that provided Rayleigh-wave group velocity maps  (Stehly et al. , 2009; Verbeke et al. , 2012). These formed a database of surface-wave group and phase velocity dispersion curves that were inverted to derive both group and phase velocity maps of central Europe.
Molinari et al. (2015)  used the database of  Verbeke et al. (2012)  to derive a 3D shear velocity model of the Alpine region and Italy. The Western Alps have also been studied with ambient noise tomography  (Fry et al. , 2010) , which yielded isotropic and anisotropic models of surface-wave phase velocity. Using surface-wave tomography from ambient noise and earthquake data,  Kästle et al. (2018)  presented a shear velocity model of the Alps. Lu et al. (2018)  also used ambient noise data to present a shear velocity model of the European crust and upper mantle. To the east of the Eastern and Southern Alps (ESA), the crustal structure of the Carpathian-Pannonian region was studied with noise tomography depicted in surface-wave group velocity and 3D shear velocity maps  (Ren et al. , 2013).
Ambient noise data We used continuous three-component seismic data recorded at 71 permanent broadband stations in the Eastern and Southern Alps from the Seismic Network of Austria  (OE, 1987) , National Seismic Network of Switzerland  (CH, 1983) ,  Italian Seismic Network (INGV, 2006) , Province Südtirol (SI, 2006), German Regional Seismic Network  (GR, 2001) ,  Bay-ernNetz, Germany (BW, 2001) , Slovenian Seismic Network  (SL, 2001) , Hungarian National Seismological Network  (HU, 1992) , and Slovak National Network of Seismic Station  (SK, 2001). In order to improve ray coverage, we completed our dataset with 19 temporary broadband stations of the AlpArray-EASI project  (AlpArray, 2015; Hetényi et al. , 2018a). The Eastern Alpine Seismic Investigation (EASI) is a collaborative seismological project that was the first AlpArray collaborative experiment between the Swiss Federal Institute of Technology, University of Vienna (Austria), and the Academy of Sciences of the Czech Republic; it ran between July 2014 and July 2015.
Waveform preprocessing The first step of any noise-based analysis requires preprocessing the continuous waveform data, which strongly affects the quality of cross-correlation functions, dispersion curves, and the resulting velocity maps. Because noise characteristics and station configurations differ for each study, no uni-versal preprocessing methodology exists. The best methodology and the various processing steps have to be tested for each dataset and are usually evaluated with basic parameters such as the symmetry of the cross-correlation function, signal-to-noise ratio, and frequency bandwidth (e. g. ,  Bensen et al. , 2007; Poli et al. , 2013). Here, we tested several classical preprocessing methods including windowing (e. g. ,  Seats et al. , 2012) , whitening (e. g. ,  Bensen et al.
(2) We removed all paths with inter-station distances smaller than 2 wavelengths at each period. (3) We applied a second SNR pass (SNR > 5) for the correlations to pass the velocity measurements to ensure that we obtain well-estimated travel times. (4) We exclude velocity measurements that were not in a range within 2 standard deviations of the mean velocity for a given period. Applying those criteria, the dispersion data for each period were extracted to be inverted to group velocity maps. Figure  3  shows an example of velocity measurements obtained at a 20 s period for Rayleigh and Love waves. The stations and paths that satisfied our criteria are shown on the map. The measurements directly show high-and lowvelocity anomalies, which are spatially stable and related to the crystalline core zone of the Alps and the sedimentary basins, respectively.
Conclusions We used 2 years of ambient noise data recorded at a set of permanent and temporary stations in the Eastern and Southern Alps with an average station spacing of 232 km in order to perform ambient noise tomography and to derive a local high-resolution Vs model of the crust. As an increment to the previously presented Vs model for the Alps  (Kästle et al. , 2018)  and for Europe  (Lu et al. , 2018) , we presented both Rayleigh-wave and Love-wave shear velocity models. Our high-resolution 3D shear velocity models show very good correlation between the velocity contrasts and geology projected to depth from the surface. The models reveal details of the crustal structure down to a depth of 40 km in higher resolutions that the previous Vs models  (Kästle et al. , 2018; Lu et al. , 2018)  have neither shown nor discussed.
Acknowledgements. We thank  Ralf Schuster ,  Edi Kissling ,  Irene Bianchi , and  Ewald Brückl  for helpful discussions and all colleagues from  IG Prague, University of Vienna , and  ETH Zürich  involved in the EASI seismic profile. A complete list of people who contributed to the  EASI  project is provided at http://www. alparray. ethz. ch/. We also thank the editor,  Caroline Beghein , and two reviewers ( Andreas Fichtner  and an anonymous reviewer) for careful and insightful reviews, constructive comments, and suggestions that led us to improve the paper. We thank  Bahar Bahrami  for digitizing the features and margins of the geological units and velocity anomalies superimposed on the tomographic images, which were digitized by QGIS. The authors thank the  Austrian Agency for International Cooperation in Education & Research (OeAD-GmbH ) for funding the  Amadée  project,  FR02/2017.
Thanks also go to the  IPGS  for its support of  Dimitri Zigone  via the 2016 IPGS internal call and to the  German Science Foundation (DFG)  for its support of  Mark R. Handy  (projects Ha  2403/19  and  20 ). We acknowledge the  Seismological Networks of Austria (ZAMG) ,  Switzerland (CH), Italy (INGV) ,  Südtirol (SI) ,  Bavaria (BayernNetz) ,  Germany (BW) ,  Slovenia (ODC) ,  Hungary (HU) , and Slovakia (SK), as well as the  German Regional Seismic Network (BGR)  for use of data as made available through the GFZ webDC data center at http://eida. gfz-potsdam. de/webdc3/. The authors thank  David Applbaum  for proofreading an earlier version of the paper. Financial support. This research has been partly supported by the  Austrian Science Foundation (FWF)  (projects  26391  and  24218 ).
For COLMAP, only few tens of homologous points were found on these image pairs, while more than 400 correspondences were established using the DOF method. As seen in Table  3  numerous point triplets and 368 point quadruplets obtained with the DOF fields enable to construct a part of the pyloric antrum region (stomach). On the contrary, only very few homologous point triplets and quadruplets were obtained with SIFT features. For this reason the COLMAP software completely failed in the reconstruction of the pyloric antrum region.
Data acquisition was nominally at 10-m resolution between 250 and 1,000 m of depth, 1-m resolution between 10 and 250 m, and increased to 0. 20-m resolution between 10 m and the surface  (Organelli et al. , 2017). Float data were downloaded from the global Argo database (ftp://ftp. ifremer. fr/ifremer/argo/). Vertical profiles of chlorophyll-a (chla) fluorescence and the volume scattering function (measured at 124°a nd at 700 nm) were obtained using a Wet Labs ECO sensor. The photosynthetically available radiation (PAR, see acronym definitions in Table  2 ) was measured with a Satlantic OCR radiometer. Raw counts for each parameter were transformed into geophysical quantities by applying the manufacturer-provided scaling factor and dark count. Then, as recommended by the Argo data management procedures  (Wong et al.
To compute stocks and net rates of POC accumulation within homogeneous water masses, density measurements were used to define different layers within the mesopelagic zone. To verify the homogeneity of these layers along the trajectory of the float, the spiciness metric was calculated according to  Flament (2002). This variable allowed us to identify changes in water masses with distinct thermohaline properties but having similar density and thus to ensure spatial and temporal homogeneity within the chosen isopycnal layers. The float remained in nearly homogeneous water masses only during the year 2016 (see supporting information), and thus, only data from this year are further considered in this study. Because the float moved significantly over the period of the analysis (Figure  1a ), Lagrangian particle tracking, based on previously validated model results, was used to further test the extent to which the float sampled homogeneous water masses during the analysis period.
Acknowledgments The authors express their gratitude to the scientists, officers, and crews of the research vessel Thuwal for logistical support and assistance onboard during the deployment of the  BGC-Argo  float. U. Langner  is cordially thanked for plotting the map of the Red Sea and  L. Legendre ,  L. Prieur ,  Peter Brewer , and  Edward Peltzer  for their advices and discussions on the results presented here and for reading the manuscript. This study is funded by the  King Abdullah University of Science and Technology (KAUST) ,  Kingdom of Saudi Arabia. The collection of the data was funded by a  European Research Council Advanced  grant (remOcean, agreement no. 246577 ) while final writing was funded by a  European Research Council   Advanced grant  ( REFINE , agreement no. 834177 ).
A set of triplicate samples was incubated in the dark at the same temperature, allowing respiration rate determination. Gross oxygen production rates were calculated for each of the light intensity applied, as the sum of the net oxygen production and absolute value of respiration rates. Carbon gross production and carbon respiration were expressed using a molar photosynthetic quotient C:O 2 of 1 (ref. 77 ) and plotted against irradiance. Evaluation of lake rates of gross primary production. Production-irradiance (PI), incident photosynthetically active radiation (PAR), underwater light field, and diurnal carbon production were calculated using the Phytotools package for R (ref. 78 ). Carbon gross production rates experimentally determined depending on light intensity were analysed using the model described by ref.
79  , and α (initial slope of PI relation at low PAR intensity), β (photoinhibition parameter at high PAR intensity) and Pmax (maximal production rate) parameters evaluated. Incident PAR was simulated, for the months of April and October, using the geographic coordinates of Dziani Dzaha, an atmospheric turbidity factor and shortwave radiation to PAR corrections  80. Underwater light field and attenuation coefficient k were calculated from discrete field measurements of PAR performed at various depths during each survey using a Li-Cor LI-193 underwater spherical quantum sensor coupled to a LI-1400 data logger. Phytoplankton gross primary production (g C m -2 d -1 ) was calculated from simulated PAR at each depth for each month, with the measured attenuation coefficients and PI parameters as described above.
Acknowledgements Research was supported by  IPGP, INSU-INTERRVIE  (Grant  AO2013-785992 ), TOTAL (convention  FR00008189 ), the  Agence Nationale de la Recherche  (project  DZIANI , Grant Number  ANR-13-BS06-0001 ),  Université de Montpellier  (projects METTRO and MYPROBE), and  Total Corporate Foundation  (project  DZAHA   C001493 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors also wish to thank their colleagues  P. Got ,  M. Bouvy ,  V. Grossi ,  M. Hugoni ,  H. Agogue  and  D. Sala  for their support and assistance during sampling campaigns. The authors also thank the  Air Austral Airline Company and Alexandra and Laurent at the 'Les Couleurs' Guest House in Mayotte  for their assistance and support during field trips; as well as the  Deep Carbon Observatory  for discussions and funding for two informative short videos about our field expeditions performed produced by " Sur une île " (https ://vimeo.
Multibeam processing and interpretation We reprocessed a subset of the multibeam bathymetry data from cruise SO186 collected using the SIMRAD EM120 Multibeam System. We applied an updated and improved sound velocity profile correction, a ship roll correction and removed spikes from individual pings, to improve the signal to noise ratio of the true seafloor topography. We then re-interpreted this data and reinterpreted multibeam data from cruise MD116.
We are therefore confident that our interpretation represents true vertical separation. The seismic horizons provide a database of TWT picks at every CDP where picks were made. We convert the fault displacement TWT measurements to metres using seismic interval velocities picked at ∼1000 CDP spacing from the original processing of the MCS reflection data by BGR. This is an additional improvement on  Geersen et al. (2015) , which used a single set of estimated interval velocities throughout the seismic lines. Around faults of particular interest, we increase the number of interpreted horizons and grid all of the observed vertical separations as a heat map (see Fig. 6  C and G).
Relationships between seismic and core-scale faulting and sediment material properties For each 20 km segment on seismic profiles A and B, we normalise the depth-displacement profile against the corresponding maximum displacement (black line, Fig. 7A ). This allows us to directly compare the pattern of displacement with depth for each 20 km segment with the IODP borehole data. By adjusting the depths of the seismic reflectors (R2-12) to Site U1480, we can plot fault displacement against various datasets from the borehole (i. e. lithology, sand fraction, age-depth relationship, fracture intensity, seismic velocity and porosity, see S4 supplementary material). Fig. 7A  shows the overall average displacement of the 20 km segments (black line) and examples (coloured lines) from detailed analyses (e.
Acknowledgements This research used data provided by the  International Ocean Discovery Program (IODP)  (www. iodp. org/access -data -and -samples) and was supported by the  Natural Environmental Research Council  [grant numbers  NE/P012817/1  and  NE/L002531/1 ]. We thank the JOIDES Resolution crew, IODP technical team and science party for their contributions during Expedition 362. We also thank  Dave Sanderson  for useful discussions, the two anonymous reviewers for their comments, which helped to improve this manuscript and editor  Jean-Philippe Avouac.
Columbus: period of contact; 1635, settlement of the first colony; 1950, end of the colony. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article. ) 25 ml mg À1 for both treatments. Each treatment lasted for 24 h, and samples were rinsed 5 times with double-deionized water. Carbon and oxygen isotope analysis of bioapatite carbonate was performed at the Laboratoire d'Ecologie des Hydrosyst emes Naturels et Anthropis es (LEHNA; UMR CNRS 5023) in Lyon using an isoFLOW system connected on line in continuous flow mode to a precisION mass spectrometer operated by a visION software from Elementar Uk Ltd. Sample powders were loaded in roundbottomed, non-evacuated LABCO Exetainer® 3. 7 ml soda glass vials. For each pre-treated sample, two aliquots of approximately 2 mg were automatically reacted with anhydrous oversaturated phosphoric acid prepared according to the protocol described by  McCrea (1950).
Radiocarbon dates were also obtained on seven bones of A. rayi (without combined stable isotopic analyses) to complete the lastoccurrence dataset (SI 4). Overall, 48 bioapatite samples (out of 49) of fossil A. rayi were directly dated by accelerator mass spectrometry (AMS). Collagen recuperation, gelatinization and graphitization were performed on the Laboratoire d'Arch eologie et d'Arch eom etrie (ARAR; UMR CNRS 5138). AMS radiocarbon measurements were performed at the Saclay laboratory (LMC 14) as part of the ARTEMIS program  (Billard, 2008).
• Volumetric approaches for surface reconstruction based on depth maps are well-established, time-efficient methods for depth sensors, also known as RGB-D  [15] , and might be a valid approach also for pure image-based approaches. The aim of this work was thus to evaluate whether the integration of visibility information (image orientation) and photo-consistency and during the meshing process can potentially lead to an improvement of the mesh quality (and successive products). For this reason, three diverse surface reconstruction approaches were considered and evaluated on diverse datasets (Figure  1 ):.
• Method 3: Given the image poses, a mesh model is generated from the depth maps produced in Method 1, employing a volume integration approach  [15, 18, 19]. Again, in this method, visibility and photo consistency information are not taken into consideration while reconstructing the surface. The results of the considered approaches were evaluated using several metrics, including accuracy, completeness and roughness. On the contrary, the computational time was not considered a key factor for this investigation. The rest of the article is divided as follows. Section 2 reviews the main concepts and steps of DIM and MVS. Section 3 provides an overview of the available DIM/MVS benchmark datasets, examining Sensors 2020, 20, 5863 3 of 24 their suitability for the present study; surface reconstruction and assessment criteria are also addressed.
• Method 3: Given the image poses, a mesh model is generated from the depth maps produced in Method 1, employing a volume integration approach  [15, 18, 19]. Again, in this method, visibility and photo consistency information are not taken into consideration while reconstructing the surface. Sensors 2020, 20, x FOR PEER REVIEW 3 of 25 The results of the considered approaches were evaluated using several metrics, including accuracy, completeness and roughness. On the contrary, the computational time was not considered a key factor for this investigation. The rest of the article is divided as follows. Section 2 reviews the main concepts and steps of DIM and MVS. Section 3 provides an overview of the available DIM/MVS benchmark datasets, examining their suitability for the present study; surface reconstruction and assessment criteria are also addressed.
DIM/MVS Benchmarks Bakuła et al. [40]  and Özdemir et al. [41]  provided overviews of benchmarking initiatives proposed in photogrammetry and remote sensing. Knapitsch et al. [42]  and Schops et al. [43]  discussed and proposed benchmarks specifically focusing on MVS. Each of the available benchmarks has unique features, which cover different scene characteristics, from small objects in laboratory conditions, such as in Middlebury MVS  [27, 44]  and DTU Robot Image Data Sets  [28, 45]  or 3DOMcity benchmark  [41, 46] , to more and complex scenes both indoor and outdoor  (Strecha [47] , ETH3D  [43, 48] ; Tanks and Temples  [42, 49] ; ISPRS-EuroSDR benchmark on High Density Aerial Image Matching  [50, 51]. Image resolutions also vary, from very small (0. 2 Mpx Middlebury MVS) to medium (6 Mpx Strecha) up to high (24 Mpx 3DOMcity and ETH3D) and very high resolution aerial images (136 Mpx of the ISPRS-EuroSDR benchmark).
Frames extracted from videos of different quality are also available (ETH3D and Tanks and Temples). Ground truth data for benchmarking MVS methods are usually acquired with laser scanner systems and used in the form of a point cloud. Strecha and Middlebury MVS convert the point cloud into a triangle mesh, yet the reference models are not publicly available from Middlebury MVS. In some cases, the ground truth is available only for "training" data, while additional scenes are provided for the evaluation (ETH3D and Tanks and Temples). Most of the evaluation procedures impose a resampling or regularization of the MVS 3D data to be evaluated and, if the submitted result is a mesh, a conversion into point cloud (DTU Robot Image Data Sets, ETH3D, Tanks and Temples) is performed. The investigated methods are often evaluated by submitting the obtained results online (e.
g. , Middlebury MVS and 3DOMcity); however, open source code is also made available for offline testing and training (DTU Robot Image Data Sets, ETH3D and Tanks and Temples). The assessment protocol requires the reference/ground truth (GT) and tested/submitted data (D) to be aligned, i. e. , co-registered in the same reference system. This may be accomplished in different ways: (i) using the provided image interior and exterior orientation parameters (Middlebury MVS and ETH3D); (ii) computing a 7-Degrees of Freedom (DoF) spatial transformation through absolute orientation of the image exterior orientation parameters (3DOMcity and Tanks and Temples); and (iii) with an iterative closest point (ICP) refinement between the reference and test data (Tanks and Temples). The common metrics used in the evaluation are accuracy and completeness (Middlebury MVS, DTU Robot Image Data Set and 3DOMcity), also defined, respectively, as precision and recall (ETH3D and Tanks and Temples).