dataesr/scientific-paragraphs-categorization huggingface dataset:
Only the appendicular skeleton of Europasaurus remains to be described of this taxon, but our OTU here is based on first-hand study of these materials by PDM. Abydosaurus, the most unstable brachiosaurid OTU in our analysis, is the only taxon for which a detailed description is currently lacking  (Chure et al. , 2010) , and we have not been able to study this material. As such, a future iteration of this data matrix that incorporates all anatomical information for Abydosaurus might enable the full resolution of brachiosaurid inter-relationships, but novel information is otherwise likely to only come from the discovery and description of new specimens. Following our revised analysis, Brachiosauridae is supported by the following synapomorphies: (1) presence of denticles on teeth (C113 (reversal)); (2) lateral fossa at the base of the prezygapophyseal process in middle cervical vertebrae (C403); (3) elongate and dorsoventrally narrow diapophyses in anterior-middle dorsal vertebrae (C154); (4) anterior dorsal neural spines that are dorsoventrally taller than posterior ones (C158); (5) middle-posterior dorsal neural spines with spinopostzygapophyseal laminae divided into medial and lateral branches (C165); (6) ribs of sacral vertebra 2 include a contribution from Sv1 (C406; Europasaurus = '?'); (7) ventral margin of coracoid without a notch anterior to glenoid (C220 (reversal)); (8) anteroposterior length of proximal plate to proximodistal length of ischium ratio of 0.
To investigate the effect of taxon sampling further, we reduced the  Carballido et al. (2015)  matrix to only those taxa included in our study (38 taxa included, with 35 taxa excluded). Aside from some loss of resolution, re-running the analysis (with Shunosaurus set as the outgroup) resulted in an essentially unchanged topology (720 MPTs of 765 steps), with Europasaurus still placed outside of Titanosauriformes. As such, taxon sampling is not the cause of the discrepancy between the matrices, and the differences must pertain to variation in character scoring and/or character sampling. Finally, we note that two taxa (Euhelopus and Tastavinsaurus) that are otherwise universally recovered within Titanosauriformes also fall out as non-titanosauriform macronarians in the  Carballido et al. (2015)  matrix (including our subsampled datasets).
ACKNOWLEDGEMENTS The authors would like to thank both the  Solvay group and Inovyn company  for allowing fieldwork in the Damparis quarry,  François Atrops  and  Raymond Enay  from  Lyon 1 University  for the identification of the ammonites, and  Elsa Cariou  from  Nantes University  for stimulating discussion. We thank  Y Després  and  V Pernègre  for help in the cleaning and re-preparation of the specimens. Specimen photographs were taken by  L Cazes  of the MNHN, with collaboration of RA. We are also grateful to all of those who have helped with access to sauropod specimens in their care. José Carballido  kindly provided us with additional information and photographs of Padillasaurus. Paul Upchurch also provided helpful discussion on interrelated characters. Reviews by  Emanuel Tschopp  and  Mathew Wedel  improved an earlier version of this work.
MONITORING Several cave monitoring sites inside and outside PD were equipped with data loggers or visited with different time intervals. Long-term data were collected from April 2014 to June 2016. Cave air temperatures were measured with sensors distributed along the cavity but only records from the outside temperature logger and records from the logger at the topographic station 2 were used in this study (respectively points TS1 and TS2 denoted on the Fig. 1  (a)). The topographic station 2 is located at 25 m from the entrance. Air temperatures were recorded at 30 min intervals) and logged externally (accuracy ±0. 1 °C). The topographic stations 1 and 2 (numbered TS1 and TS2 on the Fig. 1(a) ) were visited approximately once a month and additional air temperature, moisture balance and relative humidity were recorded with portable apparatus  Kimo HD 200 (certified).
At the same time wall temperature was measured at each location using an IR thermometer Fluke 568 IR with an accuracy of 1 %. To relate daily and seasonal microclimatic parameters monitored in Petites Dales cave to external conditions, we used daily temperature, barometric, psychrometric data from the nearby Dieppe meteorological station. This meteorological station is located at 30 km north-east from the cave and at a comparable elevation (27. 5 m for the cave, 32 m for Dieppe). Data from Dieppe, recorded 1 hour intervals between 2013 and 2015, are used for validation of the data from the data logger located outside to the cave. To better understand the karst underground climate, and especially its response to the external climate, additional data were recorded for short periods. Airflow and temperatures were measured three times for short periods  (September 21-23, 2014; October 26-28, 2015; January 31, 2015)  by digital hot-wire anemometers Thermo-anemometers HD2103.
Biomass and databases Although the ecological and biogeochemical importance of pteropods is now well recognised, their global biomass distribution remains poorly resolved. Such information is crucial for assessing the present size of pteropod populations and quantifying their contribution to the carbon export within ocean biogeochemical models. There is a wide body of pteropod net-catch data available within on-line databases (e. g. OBIS, PANGAEA, NMFS-COPEPOD, ZooDB, GBIF) with the most comprehensive being in the North Pacific, Southern Ocean and North Atlantic. Further pteropod databases are also in their early stages of construction for the Mediterranean and Arctic. A more thorough assessment of the coverage and uses of these databases are presented in the External Supplementary material "ESM_data source".
Most of the data is open access provided there is appropriate acknowledgement of its use. Those databases have the potential to be a valuable tool to improve the assessment of the effects of anthropogenic OA, particularly in terms of the vulnerability of calcifying species, since it provides a benchmark against which modelling projections and future sampling efforts can be compared. Bednaršek et al. (2012a)  have provided a comprehensive global data coverage of pteropod distribution. The study was part of the MARine Ecosystem DATa project (MAREDAT), which aimed to construct a database of field measurements to determine the biomass of plankton functional types  (PFTs, Le Quere et al. , 2005). The pteropod database (doi. pangaea. de/http://dx. doi. org/10. 1594/PANGAEA. 777387) was one of 10 PFT databases set up as part of this initiative, which are now all publicly available (https://www.
uea. ac. uk/green-ocean/data# biomass). The  Bednaršek et al. (2012a)  study amassed data from net catches to a maximum depth of 2000 m between the years 1950 and 2010 from PANGEA, ZooDB, NMFS COPEPOD as well as 41 scientific articles. This amounted to 25,939 data points from 15,134 net-sampling stations. Details on pteropod abundance, biomass, species composition and life stages were documented within the database, alongside sampling information (i. e. maximum net depth, net mesh size as well as time of the year/day, collection methods etc. ). Although pteropod observations were available for all ocean basins (Fig. 2 ), there was a clear bias of the data towards observations in the Northern Hemisphere (NH) (85% of entries), with the remaining 15% in the Southern Hemisphere (SH). Based on these results, it was estimated that shelled pteropods constituted a mean global carbonate biomass of 23.
17 mg CaCO 3 m -3 and they are especially prevalent in the coastal to shelf areas (Fig. 2 ). By extrapolating regional biomass estimates to a global scale,  Bednaršek et al. (2012a)  estimated global pteropod biomass to amount to 500 Tg C, contributing between 20 and 40% to global carbonate production. Berner and Honjo (1981) , by comparison, considered that pteropods may constitute at least 12% of the total carbonate flux worldwide. This is a significant number since it corresponds to five times the global estimated planktonic foraminifera biomass production  (Schiebel and Movellan, 2012)  and around one fifth of the global diatom production  (Leblanc et al. , 2012). Unfortunately, most databases contain information on pteropod distribution and abundance but not on biogeochemical parameters and processes, such as aragonite production, shell dissolution, or inorganic to organic carbon ratios.
Hence careful consideration of the likely densities and scales of variability is warranted in employing small-volume optical methods  (Benfield et al. , 1996). High frequency active acoustic methods likewise can offer an attractive means of sampling thecosome pteropods remotely since the hard shell makes them efficient scatterers of sound relative to other similarly sized zooplankton (ca. two orders of magnitude stronger returns than for a crustacean of similar size,  (Stanton et al. , 1996). The fast sampling rates afforded by acoustic methods offer the potential for rapidly sampling pteropods throughout the water column at fine resolution, but only few studies have capitalized on acoustic methods for ecological investigations. Tarling et al. , 2001 , for instance, characterized the speed and timing of diel vertical migration of scattering layers associated with Cavolina inflexa in the Ligurian Sea using a shipmounted acoustic Doppler current profiler, while ongoing analyses of multi-frequency acoustic data collected in the northwest Atlantic Ocean allow the characterization of Limacina retroversa swarm structure and distribution (Gareth Lawson, 2017, personal communication).
Acknowledgements We are grateful for support from the  UK Ocean Acidification research programme (UKOA) , co-funded by  NERC ,  Defra  and  DECC. We also thank  Phillip Williamson  and  Carol Turley  for their support during the organization of the "Response of pteropods to OA and climate change" workshop. M. I. Berning  is financed by the  German Research Foundation   Priority Programme 1158 Antarctic Research  with  Comparable Investigations in Arctic Sea Ice Areas ( Project  DFG-1158 SCHR 667/15-1 ).
Published by Copernicus Publications on behalf of the European Geosciences Union. G. Parard et al. :  Air-sea CO 2 fluxes Other studies have calculated FCO 2 across ocean basins using climate databases  (Takahashi et al. , 2002)  or biogeochemical numerical models  (Lenton et al. , 2013; Arruda et al. , 2015). These calculations, however, have failed to provide outputs covering the global coastlines. This is primarily due to the sparseness of the temporal and spatial data sets (such as pCO 2 of the surface ocean or wind fields). The wide range of values of in situ coastal FCO 2 entail even wider uncertainties in global estimates of FCO 2 , as there is the potential to under-or overestimate FCO 2 when performing a spatio-temporal integration  (Wollast, 1991; Takahashi et al. , 2009; Ribas-Ribas et al.
Wind products In this study we used wind products to calculate the transfer velocity, based on a mesoscale reanalysis product. A reanalysis is a combination of measurements and a model in which the available data are assimilated into a high-quality numeric modelling system. The reanalysis used in this paper was provided by the Swedish Meteorological and Hydrological Institute (SMHI) with the High Resolution Limited Area Model (HIRLAM) geometry (22 km horizontal grid spacing and 60 levels in the vertical; the model top is at 10 hPa)  (Soci et al. , 2011). HIRLAM is downscaled and dynamically adapted to a higher resolution (5 km grid) with a simplified HIRLAM called the Dynamic Adaptation Model (DYNAM). The observations of 10 m winds assimilated into the system are from four databases: the Integrated Surface Database (ISD) Station History maintained by NOAA's National Centers for Environmental Information (NCEI), the Meteorological Archival and Retrieval System (MARS) at ECMWF, the European Climate Assessment & Dataset (ECA&D) used as input for E-OBS version 6.
0, and the national climate databases of SMHI and Météo France (MF). The temporal resolution is 6 h. In the following, this product will be referred to as SMHIp. The method requires for the explicative data to stay coherent in terms of resolution, and as such we chose a temporal and spatial resolution of monthly 4 × 4 km pCO 2 pixels. In order to estimate the impact of the wind product on the air-sea CO 2 flux, we computed the flux with a remote sensing product on a daily scale. The wind data are reprocessed QuikSCAT (QSCAT) and ASCAT data  (Bentamy and Croizé-Fillon, 2013)   The flux of CO 2 (FCO 2 ) from sea to air (positive value) or air to sea (negative value) is often calculated using the difference in the partial pressure of CO 2 between the surface water and the atmosphere ( pCO 2 ).
Acknowledgements. We thank  Tiit Kutser  and  Melissa Chierici  for their help. We would also like to show our gratitude to  Sylvie Thiria  for sharing her pearls of wisdom with us during the course of this research. This research was supported by the  Swedish National Space Board  (grant no. 120/11:3 ). Edited by: Benjamin Smith Reviewed by: two anonymous referees.
Hydrodynamic (waves and tides) data were recorded in-situ, and established two different cases of water elevation models as a basis for shoreline elevations. DEMs were created and tested against LiDAR. Statistical analysis of shoreline elevations and migrations were made, and morphological variability was assessed between both surveys. Results indicate that the best shoreline elevation model includes both the significant wave height and the mean water level. Low energy conditions and in-situ hydrodynamic measurements made it possible to produce video-derived DEMs virtually as accurate as a LiDAR product, and therefore make an effective tool for coastal managers.
LiDAR Survey and Topographic Data A Mobile Terrestrial LiDAR System (MTLS), hereinafter referred to as LiDAR, was used to acquire a high-resolution beach topography on 3 August 2016 (Figure  4 ) and 18 November 2016. Both surveys were conducted at low tide. The LiDAR is mounted on a utility side by side vehicle (Figure  5b ) moving at ~30-40 km/h. The system includes a laser scanner Riegl VQ-250 and a GPS inertial navigation system Applanix POS-LV 220. Post-processing for raw data analysis is described in Didier et al. [62]. A manual filtering of the debris on the beach was made on the raw LiDAR data (dense point cloud) to delete trees and driftwood from the final LiDAR topography. The RMS errors (x, y, z) (23 control points) between RTK-GPS (Trimble R8) and adjacent laser points was assessed in Didier et al.
[62]  and are in the order ±0. 03 m  [62]. The LiDAR data were used as a validation surface for each detected and orthorectified shoreline. During the autumn of 2016 (September to December), more energetic events were observed. Offshore significant wave heights reached a maximum of 3. 8 m at G2 on 12 November, but it happened at low tide and dissipation across the tidal flat prevented incoming waves to reach the beach. On 16 November, waves at the beachface toe reached 0. 92 m, and mean wave directions (observed offshore) spanned between 99 • and 123 • (ESE) throughout the day.
Shoreline Detection and Water Elevation Models Five-minute video segments were acquired during the rising tide of 3 August 2016 between 12:50 and 16:00 (EST) and on 18 November 2016 between 14:00 and 17:30 (EST). LiDAR surveys were conducted simultaneously with video acquisitions. All image analyses were performed using Matlab. Outputs are 5-min time-averaged images (TIMEX) used for shoreline detections based on the presence of swash at the water/beach junction. The shoreline detection is applied in two steps  [38]. As a first approximation, all shorelines were automatically extracted based on a blue, green and red color ratio (B + G/R) on all TIMEX images, where B, G, and R are respectively the pixel intensity values of the blue, green, and red channels. The processing was computed for all pixels in a pre-defined region of interest (ROI) on the beach (Figure  6a ).
The shoreline represents a local minimum in B + G/R ratios over the entire ROI, with lower ratio values (pixel ratio values <1) corresponding to the beachface while the water surface falls under higher ratios, typically over 1. The next step is the shoreline detection and identification on the TIMEX images. The technique is based on the alongshore shape of the swash on the beach in the ROI and detects the water/land boundary rather than dry/wet sand contours  [38]. Falsely detected shorelines due to sunshine reflectance on the water, debris at the shoreline, and people on the beach were discarded, but no manual intervention was applied in the delineation. Remote Sens. 2017, 9, 826 9 of 22 conducted simultaneously with video acquisitions. All image analyses were performed using Matlab. Outputs are 5-min time-averaged images (TIMEX) used for shoreline detections based on the presence of swash at the water/beach junction.
Acknowledgments: The authors thank the  Québec Ministry of Public Security  for funding the project. D. Didier  received a scholarship from the  Fonds Québécois de  la recherche sur la nature et les technologies. The project would not have been possible without the research assistants from the  LDGIZC (Canada)  and  LGO (France). The authors greatly acknowledge the coastal citizens of  Pointe-Lebel  for their support and constant surveillance of the video station. We would like to acknowledge five anonymous reviewers for their helpful suggestions and comments.
, 2017). Therefore, the high sulfate-to-OA ratio is an indication of the volcanic origin of the probed aerosol. Backward trajectories were calculated from several points along the flight paths. Figure  6  shows backward trajectories started at nine points along the AC14 flight track where sulfate-to-OA ratios larger (colored lines) or smaller than 1 (gray lines) were detected; the flight and aerosol data measured at each point are presented in Table  1. Figure  6  clearly shows that the (colored) trajectories, with one exception, initialized within the sulfate plume originate from central Africa, whereas the gray trajectories, started outside of the sulfate plume, appear to originate from South America or from more southern regions over or across the Atlantic Ocean. The air mass trajectory analysis indicates that the AC14 observations were likely the result of probing the volcanic plume emitted on 7 September, the first one detected by OMI (see Fig.
Acknowledgements This work has been supported by the  Max Planck Society (MPG)  and the  Paul Crutzen Graduate School (MPGS). For the operation of the ATTO site, we acknowledge the support by the  German Federal Ministry of Education and Research  ( BMBF  contract  01LB1001A ) and the  Brazilian Ministério da Ciência, Tecnologia e Inovação (MCTI/FINEP  contract  01. 11. 01248. 00 )as well as the  Amazon State University (UEA) ,  FAPEAM, LBA/INPA  and  SDS/CEUC/RDS-Uatumã. We acknowledge the generous support of the ACRIDICON-CHUVA campaign by the  Max Planck Society , the  German Aerospace Center (DLR) ,  FAPESP (São Paulo Research Foundation ), and the  German Science Foundation (Deutsche Forschungsgemeinschaft, DFG). This study was also supported by  EU  Project  HAIC  under  FP7-AAT-2012-3.
Moreover, applying Bayesian age modeling  (Haslett and Parnell, 2008)  based on these high-precision data sets allows us to model variations in sediment accumulation rate and to directly compare other proxy data across different PTB sections, inclusive of the Meishan GSSP. Our data demonstrate that the PTB, as recognized in our sections by conformable boundaries between late Permian and basal Triassic formations, is synchronous within analytical uncertainty of ca. 40 ka. We also show that Bayesian age models produce reproducible results from different sections, even though U-Pb datasets originate from different laboratories. We construct a coherent age model for the PTB in Dongpan and Penglaitan, which is also in agreement with the PTB age model from Meishan  (Burgess et al. , 2014). These results further demonstrate that 206 Pb / 238 U dates produced in two different laboratories using the EARTHTIME tracer solution provide reproducible age information at the 0.
Bayesian chronology In this study we use Bayesian interpolation statistics to establish a probabilistic age model based on our high-precision U-Pb zircon dates of each individual ash bed and its stratigraphic position, as it is incorporated in the free Bchron R software package  (Haslett and Parnell, 2008; Parnell et al. , 2008)  to constrain the chronological sequence and sedimentation history of the investigated sections. By assuming normal distribution of our U-Pb dates within one sample and based on the principle of stratigraphic superposition, which requires that any stratigraphic point must be younger than any point situated below in the stratigraphic sequence, it models the age and its associated 95 % confidence interval for any depth point within the studied sedimentary sequence. The model is based on the assumption of random variability of sediment accumulation rate, yielding a family of dispersed piecewise monotonic sediment accumulation models between each dated stratigraphic horizon.
In this study we use the Bayesian Bchron model as it is part of the Bchron package (http://cran. r-project. org/web/ packages/Bchron/index. html). This model outperforms other Bayesian age-depth models, as shown by an extensive comparison conducted on radiocarbon dates from Holocene lake sediments  (Parnell et al. , 2011). It provides a non-parametric chronological model according to the compound Poissongamma model defined by  Haslett and Parnell (2008) , requiring the weighted mean 206 Pb / 238 U age and the stratigraphic position of the investigated ash beds as input parameters. Since the Bchron model was initially coded for radiocarbon dating with a commonly unknown duration of accumulation for a radiocarbon-dated bed, it also allows for the input thickness of such a horizon to be defined.
Sample DGP-21 This volcanic ash bed was sampled 0. 1 m above the formational boundary. Fourteen zircons were dated, among which the eight youngest yield a cluster with a weighted mean 206 Pb / 238 U age of 251. 953 ± 0. 038/0. 075/0. 28 Ma (MSWD = 0. 26). The six oldest grains display an inherited component as suggested by their scattered 206 Pb / 238 U dates ranging from 252. 145 ± 0. 120 to 252. 715 ± 0. 084 Ma. The U-Pb data of DGP-21 have already been published in a companion study  (Baresel et al. , 2016)  that deals with the stratigraphic correlation of ash beds straddling the PTB in deepand shallow-marine successions of the Nanpanjiang Basin.
Age-depth models Figure  5  shows a comparison of three different age-depth models based on linear interpolation, cubic spline interpolation and Bayesian statistics, each applied to exactly the same U-Pb dataset of Dongpan (Fig. 5a ) and Penglaitan (Fig. 5b ). As discussed in the Methods section, the Bayesian Bchron model leads to more realistic uncertainty estimates, producing an increased uncertainty of the model age with increasing distance from the stratigraphic position of a U-Pb dated sample. Due to the well constrained U-Pb dates of Dongpan and Penglaitan, all three age-depth models predict (within uncertainty) similar ages for the PTB in Dongpan (Fig. 5a ) and Penglaitan (Fig. 5b ). Given that the Bayesian Bchron model evaluates the age probability distribution of each U-Pb date with respect to the other dates of the sequence, it provides a more robust and better constrained chronology, which even results in smaller uncertainties of the predicted model dates compared to the standard linear regression models (as indicated by the smaller uncertainty of the Bchron model age for the PTB in Dongpan and Penglaitan).