dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to evaluate the accuracy and stability of the best models based on reflectance measurements, the MMELS dataset was divided into calibration and independent validation subsets. The models were calibrated using data collected from 2/3 of the total number of stations, corresponding to the first two field campaigns for each lagoon, and were validated using data collected from 1/3 of the stations, which corresponded to the last field campaign for each lagoon. Validation analysis was not extended to smaller subsets (e. g. , Mundaú lagoon, Manguaba lagoon or optical classes) in order to reduce risks associated with obtaining non-representative samples for both calibration and validation sets.
Constituent Concentrations Notably, the chlorophyll-a and TSS concentrations in the water samples exhibited high variability (Table  3 ). The levels of chlorophyll-a differed between the lagoons, ranging between 0. 97 and 48. 9 mg/m 3 (average value of 12. 86 mg/m 3 ) in Mundaú lagoon, and between 5. 99 and 117. 54 mg/m 3 in Manguaba lagoon (average value of 42. 77 mg/m 3 ). In contrast to the Chl-a concentrations, the TSS concentrations were higher in Mundaú lagoon (range of 15. 2-61. 0 mg/L and average of 32. 8 mg/L) in comparison with Manguaba lagoon (range of 9. 0-44. 0 mg/L and average of 22. 7 mg/L). In addition, we did not find a strong correlation between the chlorophyll-a and TSS concentrations in the MMELS (r 2 = 0. 08; data not shown).
Reflectance Spectra and Classification The measured reflectance spectra (range of 400-720 nm) of the surveyed sampling points in the Mundaú and Manguaba lagoons are shown in Figure  2a ,b, respectively. The hyperspectral reflectance differed slightly between the two datasets. Nevertheless, in both lagoons, it was possible to identify spectral features similar to reflectance spectra previously observed for turbid, productive waters  [32, 59] , such as a slight depression at 440 nm; a prominent peak around 565 nm; a trough at 625 nm (more intense in Manguaba lagoon) followed by a discrete peak around 660 nm; and a prominent trough at 670 nm followed by a prominent peak close to 705 nm. With respect to Manguaba lagoon, we observed that the reflectance magnitude peak around 705 nm was comparable to that in the green region, except for the 14 July campaign, where we did not observe a well-defined peak in the green region.
Assessment of Chl-a Retrieval Models A summary of the accuracy assessment of the best band models for classified and non-classified waters-considering three distinct datasets (i. e. , Mundaú lagoon, Manguaba lagoon and MMELS)-can be observed in Table  4. Table  4. Slopes (p) and intercepts (q) of the linear best-fit function between observed and retrieved chlorophyll-a with corresponding coefficient of determination (r 2 ) and root mean square error (RMSE in mg/m 3 ) for the monitoring datasets. The MMELS dataset (global) was divided into calibration (cal) and independent validation (val) subsets to evaluate the accuracy and stability of the best models based on the reflectance measurements. For non-classified waters, the blue-green ratio model Equation (  5 ) showed a very poor correlation with the measured Chl-a concentration for the MMELS dataset (r 2 = 0.
Interestingly, for both of these datasets, the optimal positions for λ 3 and λ 4 were close to the upper limit of the operating range. In addition, the distance between these two wavelengths was only 1 nm, which is considerably smaller than the resolution of the radiometers used in the study. Naturally, such positioning aimed to minimize the effect of absorption by CDOM and NAP, as well as backscattering, in λ 3 , but clearly this was not sufficient, especially for Manguaba lagoon water samples with higher concentrations of chlorophyll-a. This resulted in a decrease in performance of the four-band model in relation to the three-band model. In general, optimal values of λ 4 are found around 730 nm  [38, 44] , which are beyond the spectral range of operation of this study. The same pattern was not observed in Mundaú lagoon, where an increase in model complexity (i.
Regarding the evaluated models for classified waters, the inversion algorithms applied to homogeneous groups generally did not improve the chlorophyll-a retrieval in comparison with the band models using the non-classified dataset; this may be associated with the following: (a) the derivation of models from a reduced number of water samples in each class; (b) the classification method chosen (e. g. , unsupervised classification clustering, fuzzy logic classification and reflectance shape characteristics calculation approach), which may control the number and optical characteristics of each class  [38, 54, 55] ; and (c) a higher uniformity of both optical characteristics and concentration values of the water constituents in each class, which may result in a lower performance of the models, mainly for classes with higher concentrations of suspended matter and lower concentrations of chlorophyll-a.
However, the pre-classification of the reflectance spectra dataset into homogeneous groups has shown to be satisfactory to validate the band models using a different handheld dataset without repositioning of optimal wavelength values for each model  [38, 48]. Therefore, the classified band models obtained for Manguaba lagoon can be considered promising, especially the model for Class 2. Although estuarine environments have more intensified dynamics in relation to other inland water bodies (i. e. , lakes and reservoirs), the high retention times of both the Mundaú (>2 weeks) and Manguaba (>1 month) lagoons suggest slight water quality changes over time. Thus, MMELS demonstrates considerable potential for the application of models using satellite sensors with spatial and temporal resolutions less than 500 m and one week, respectively, similar to the sensors evaluated in this study.
Regarding Class 2 waters, a decrease in performance of the four-band model based on simulated MERIS and OLCI bands in comparison with the two-and three-band models can be explained by the inability to identify a good combination of simulated satellite bands near the wavelengths chosen as optimal for the models using R rs measured in-situ. The assessment of models to directly estimate chlorophyll-a using satellite images captured by the MSI and OLCI sensors encourages further future investigation in MMELS, since images from these recently spaceborne sensors were not available during the monitoring period (May to September 2015). Despite the spectral range operational constraints, our findings suggest that empirical models based on optical properties involving water constituents have a strong potential to estimate chlorophyll-a using spectral data from satellite, airborne or handheld sensors in productive tropical estuaries, as was also observed in temperate, tropical and subtropical inland waters  [14, 30, 33, 68].
Despite the wide range of measured chlorophyll-a in the dataset (0. 97-117. 24 mg•m -3 ), the monitoring period was limited from May to September 2015, which corresponds to an inter-seasonal period between the end of the wet season and the beginning of the dry season. A recent study, using approximately 100 water samples for each season, indicated that a unique model based on spectral reflectance can be used to estimate chlorophyll-a for all four seasons  [42] , suggesting that the empirical models described in this study can also be applied outside of the monitoring period.
Acknowledgments: We are grateful to institutional grants provided by the  Agency for Financing Studies and Projects (Finep) , research project No. 01. 13. 0419. 00 ,and  The Brazilian National Council for Scientific and Technological Development (CNPq) , through the  Universal Research Project  ( 476789/2013-3 ), which funded all equipment and operating expenses of this study. We also thank the  Research Support Foundation of the State of Pernambuco (FACEPE) , who granted a doctoral scholarship ( PBPG-1148-3. 01/12 )to the first author, and  IMA/AL , who provided the boat that was used for collecting the in situ data. The authors would also like to thank the anonymous referees, who helped to significantly improve the quality and the clarity of this manuscript.
Vertically polarized P waves will become stronger as ray path curvatures increase for strong vertical seismic velocity gradients as well as larger source-station distances and elevation differences, but this effect is assumed to be of secondary importance. Ground motion amplitude is estimated via the RMS of 100 s seismogram time windows and A 0 is varied between 500 and 1500 times the RMS maximum measured throughout the network. Fit quality is quantified with the variance reduction defined as VR = 1 - (data -fit) 2 data 2 • 100 %, (3) where in our case "data" refers to the RMS of the 100 s seismogram time windows and "fit" refers to the calculated ground motion (Eq. 1). The summation is carried out over the nine available seismometers. Equations (  1 ) and (3) are applied in a spatial grid search over geographic coordinates and the maximum in variance reduction (100 % represents a perfect fit) indicates the source of the recorded seismic signal.
Epifluorescence and confocal laser scanning microscopy observations were carried out at the Institut de Physique du Globe, laboratoire de géomicrobiologie, Paris, France, with an Olympus FluoView1000 confocal microscope and an objective UPSLAPO 40XO NA: 0. 90. Fluorescence confocal images were obtained with concomitant excitation at wavelengths of 405, 488, and 543 nm by collecting the emitted fluorescence between 425-475, 500-530, and 560-660 nm, respectively. The images were acquired, visualized and processed using the F10-ASW FLUOVIEW software (Olympus). Raman spectroscopic analyses were carried out at the Institut de Physique du Globe, laboratoire de géomicrobiologie, Paris, France, with a Renishaw In Via Raman spectrometer coupled to an Olympus FluoView1000 confocal microscope and a 785 nm laser source.
Epifluorescence images were obtained using an Olympus cube UV bande large U-MWU2 and a 50X Objective. Beam centering and Raman spectra calibration were performed daily on a Si-Al microprocessor chip displaying a characteristic Si Raman shift at 520. 4 cm -1. Extended mode analysis in the range of 100-4,000 cm -1 was used to acquire spectra. Light was dispersed by a holographic grating with 1,200 grooves/mm and the signal analyzed with a RENCAM charge-coupled device (CCD) detector. Laser intensity set as 1%; the exposure time was 10 s for each spectrum. All of these operations were performed with Renishaw software Wire 2. 0. Aliquots of the bulk rock samples were powdered to <60 µm using a ring and puck mill at the Biogéosciences Laboratory of the Université de Bourgogne in Dijon. X-ray diffraction (XRD) was performed on both the bulk cryocarbonate and bedrock powders using a Bruker AXS D4 Endeavor diffractometer with CuK α radiations, a LynxEye detector and Ni filter, with a voltage of 40 kV and an intensity of 25 mA at the Biogéosciences Laboratory (Université de Bourgogne in Dijon, France).
ACKNOWLEDGMENTS This work is a contribution to the  ANR  project  VIP-Mont-Blanc  ( ANR-14-CE03-0006 ). We gratefully acknowledge support from the  Université de Bourgogne FABER Program (France)  and the  Institut National des Sciences de l'Univers (CNRS, France). We would like to thank  Anaïs Daburon  for assistance in the laboratory and field,  Frédéric Hebst  for SEM expertise and  Philippe Blanc  for the thin sections. We also thank the reviewers for their constructive comments and Elodie, whose contributions was critical to this discovery. Felix Ng  is acknowledged for handling this manuscript and for numerous helpful suggestions.
Acknowledgements. The hardware used in the computational studies is part of the  UMBC High Performance Computing Facility (HPCF). The facility is supported by the  US National Science Foundation  through the  MRI program  (grant nos. CNS-0821258  and  CNS-1228778 ) and the  SCREMS program  (grant no. DMS-0821311 ), with additional substantial support from the  University of Maryland, Baltimore County (UMBC). See https://hpcf. umbc. edu/(last access:  12 June 2018 ) for more information on HPCF and the projects using its resources. Edited by: Manfred Wendisch Reviewed by: two anonymous referees.
Instrumentation and data processing 2. 2. 1 Cloud instrumentation The cloud ground based instrumentation used during CLIMSLIP-NyA was installed on a measurement pole and is presented in Figure  2. The cloud optical and microphysical properties were thus assessed by three independent instruments: a PMS Forward Scattering Spectrometer Probe (FSSP-100), a Cloud Particle Imager (CPI) and a Polar Nephelometer (PN). They were all connected to the same pump by plastic tubes, leading to the sampling volume indicated on Figure  2. They were operated approximately 2 m above the platform level and mounted on a tilting and rotating mast, allowing them to be moved manually in the prevailing wind direction. The proper alignment of their inlet with the flow was based on the wind direction measurements performed by a mechanical and ultrasonic anemometer.
Figure16: Same as Figure6(a and b) for the BS cases. Even if the resuspension of crystals in the air can modify the shape by impacts, we consider that the sampled crystals are similar to the deposited precipitations and aged for several days. Thus, the BS events during CLIMSLIP were excellent occasions to measure the arctic snow properties. Figure16. b displays the average phase functions of the BS cases. The shape of the curves are very similar to the precipitation cases, typical of ice particles, but with lower g values. These measurements constitute a unique database to develop parameterizations of the arctic snow optical properties. Indeed, in most of the climate and weather forecast models, the computation of the snow albedo uses the approximation of spherical snow grain (Wang and Zeng, 2010).
m. s oscillation velocity (u; = u1 I. fi). Data on the relative decrease of the friction coefficient (absolute value) estimated from the average angle of avalanche in our rotating drum experiments are plotted in Fig. 4  as a function of f3Yu;. To calculate f3Y average values of particle size were taken as dp = 63 Jlm, 95 Jlm, 188 Jlm, 325 Jlm, 750 Jlm, and 1. 5 mm, respectively. The particle volume fraction for each sample in the drum was calculated from the volume filled and the solid density. In the case of the finest ignimbrite, the particle volume fraction is small (¢ "' 0. 25) as typical for fine cohesive powders  [6]. As particle size is increase,¢ increases up to ¢ "' 0. 4 for the noncohesive samples. This is still a low value as compared with the packing fraction of randomly packed noncohesive hard spheres(¢ "' 0.
Exploitation of available RES dataset around Dome C Based on radar equations,  Zirizzotti et al. (2012)  proposed a method to recognize wet areas using the Italian RES dataset collected around Dome C. These authors used a linear model of electromagnetic wave adsorption in the ice column, based on analysis of the EDC ice core, to account for the differences in amplitude (in dB) between echoes at the surface of the ice and at the bottom. The thresholds used to ascribe an echo to a wet or dry basal contact were ≥ 7. 7 and < 1 dB respectively. Figure  1  shows the distribution of dry and wet areas under Dome C, which reveals interesting patterns: (i) the Concordia trench is characterized by the presence of wet points only, which is a consequence of the very thick ice; (ii) the tops of the two main bedrock reliefs (candidate A and candidates B -C -D) appear to be dry; (iii) almost all the northern points are dry despite the very thick ice at this location; (iv) in between, and in particular under the EPICA drilling site, dry and wet points are scattered, with no clear trend.
The dataset available from  Zirizzotti et al. (2012)  provides information on whether melting occurs today but not on its amount and its temporal and spatial variations. The only way to obtain this information is first to retrieve the spatial distribution of GF and then to use it to infer basal melting over time. Here we present a 1-D heat model forced by reconstructed climatic conditions, which we ran in two ways. First, we solved an inverse problem with the model to infer the value of the GF in the Dome C region, using radar echoes as observational constraints. The pattern of wet and dry areas made it O. Passalacqua et al. : Geothermal flux and basal melt rate in the Dome C region possible to estimate a critical ice thickness that corresponding to a threshold between frozen and thawing ice.
6 : m = -5. 148 × 10 -7 H 2 + 4. 688 × 10 -3 H + 89. 519 g + 3. 08 × 10 -3 p 2 -5. 887 × 10 -2 p -14. 335. ( 15 ) The performance of the emulator was assessed by comparing the melt rate output from the thermal model with the one computed with the emulator. The average absolute error was 0. 014 mm a -1 and the corresponding standard deviation was 0. 016 mm a -1 , so that the errors due to the emulator were significantly lower than the corresponding uncertainties due to the GF and p. This polynomial function was thus considered to be sufficiently precise for our purpose. Here we emulated the values of m with H given by the Bedmap 2 dataset, but when a refined bedrock dataset is available, this emulator will be easy to use to compute updated estimations of the basal melt rate. Figure  7  shows the basal melt rate corresponding to the GF field interpolated from the central values of g inferred at each measurement point (called ˆ m g ) and the central value of p (p = 3.
To facilitate the comparison, we slightly tuned the imposed GF field, within the uncertainty range. The map in Fig. 9  was built with ˆ m g -1 mW m -2. Superimposed on the observed data, the model output shows that large-scale patterns of wet-dry areas were respected, especially on steep bed slopes (candidate B, C, D and to a lesser extent candidate A). However, at certain points on these bed reliefs, there was a discrepancy between model outputs and observed data, but the gap in the critical thickness was often close to 0 m (D3, D5, D8, M3), meaning that a small change in GF forcing, or a better description of the ice thickness, would assign these particular points more accurately. The 1 km resolution of the Bedmap 2 bedrock dataset  (Fretwell et al. , 2013)  smoothed the along-track subkilometric features detected by our RES survey.
The GF field is also compatible with the values inferred by  Siegert and Dowdeswell (1996)  to model the temperature above subglacial lakes around Dome C (41-58 mW m -2 ). But their values were only minima compatible with ice at the pressure melting point, whereas the present work benefits from the dry areas detected by RES used as upper-bound constraints. At the EPICA drilling site, we also computed an average melt rate of 0. 32 ± 0. 25 mm a -1 , and this value seems to be slightly lower than, yet consistent with, the value of g -1 mW m -2. The ice thickness DEM is taken from the Bedmap 2 dataset  (Fretwell et al. , 2013). Isocontours show the time lag before the climatic signal to reach the bed. 0. 56 ± 0. 19 mm a -1 previously inferred by  Parrenin et al. (2007) , with the inverse model used for dating the ice core.
Acknowledgements. We thank  Kenny Matsuoka  and two anonymous reviewers, whose fruitful comments greatly improved the manuscript. This publication was generated in the frame of  Beyond EPICA-Oldest Ice (BE-OI ). The project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 730258  ( BE-OI CSA ). It has received funding from the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under contract number  16. 0144. It is furthermore supported by national partners and funding agencies in  Belgium, Denmark, France, Germany, Italy, Norway, Sweden, Switzerland , the  Netherlands  and the  United Kingdom. Logistic support is mainly provided by  AWI ,  BAS ,  ENEA  and  IPEV. This publication also benefitted from support by the  CNRS/LEFE  project  CO2Role  and by the  Université Grenoble Alpes  in the framework of the proposal called Grenoble Innovation Recherche AGIR.
Full preparation and description of this specimen, known from teeth, vertebrae and most of the appendicular skeleton of a single individual, recognises it as a distinct taxon: Vouivria damparisensis gen. et sp. nov. Phylogenetic analysis of a data matrix comprising 77 taxa (including all putative brachiosaurids) scored for 416 characters recovers a fairly well resolved Brachiosauridae. Vouivria is a basal brachiosaurid, confirming its status as the stratigraphically oldest known titanosauriform. Brachiosauridae consists of a paraphyletic array of Late Jurassic forms, with Europasaurus, Vouivria and Brachiosaurus recovered as successively more nested genera that lie outside of a clade comprising (Giraffatitan + Sonorasaurus) + (Lusotitan + (Cedarosaurus + Venenosaurus)). Abydosaurus forms an unresolved polytomy with the latter five taxa.
PHYLOGENETIC ANALYSIS Dataset We have revised the scores for Vouivria damparisensis in the 'LSDM' data matrix of  Mannion et al. (2013) , using the latest augmented version of this matrix presented in  Poropat et al. (2016). Emended scorings for Europasaurus follow new information presented in  Carballido & Sander (2014)  and  Marpmann et al. (2015) , and those for Lusotitan and Sonorasaurus follow  Mocho, Royo-Torres & Ortega (2016) and D'Emic, Foreman & Jud (2016) , respectively. We have revised scores for the Middle Jurassic Moroccan taxon Atlasaurus  (Monbaron, Russell & Taquet, 1999)  based on casts stored at the MNHN (cranial remains, as well as elements of the manus and pes). We have also incorporated the recently described sauropod Padillasaurus, from the late Early Cretaceous of Colombia, which was recovered as a brachiosaurid by  Carballido et al.
, 2015) , and that its members were present in approximately contemporaneous deposits to Vouivria (i. e. , the Late Jurassicearliest Cretaceous of Europe). Our scores for Turiasauria are based on the following sources: Turiasaurus (Royo-Torres,  Cobos & Alcalá, 2006; Royo-Torres & Upchurch, 2012; CPT-1195 -1210 : PD Mannion, pers. obs. , 2009) ; Losillasaurus  (Casanovas, Santafe & Sanz, 2001; MCNV Lo: PD Mannion, pers. obs. , 2009) ; and Zby  (Mateus, Mannion & Upchurch, 2014; ML 368: PD Mannion, pers. obs. , 2012). Nineteen additional characters have been added to the data matrix, nine of which are novel to our study. The resultant data matrix comprises 77 taxa scored for 416 characters. Characters 1-397 are the same as those presented in previous iterations of the data matrix  (Mannion et al.