dataesr/scientific-paragraphs-categorization huggingface dataset:
Some of the analyses were performed on a Cameca SX100 at Camparis in Université Paris 6. Operating conditions were 15 kV accelerating voltage, 20 nA beam current and a beam diameter of 5 μm for white micas, amphiboles and epidotes, and of 1 μm for all other minerals. Both natural and synthetic standards have been used. The raw data were corrected with the ZAF procedure. The relative analytical error for majorelements is less than 1%. Peak counting times are 10 s for Na, Mg, Al, Si, Ca, K, Ti, and Ba, 30 s for Mn and Fe, and we used half of the peak time for background counting. Element distribution maps were measured in WDS (Si, Al, Ca, Fe, Mn, Mg, Cr, Ti, Na, K) and energy-dispersive spectrometer mode with an acceleration voltage of 15 kV, a beam current of 20 nA. We used different pixel size (1-10 μm) and dwell time (70-100 ms) depending on the mapped area.
7 cm -1 peak in both cases. To ensure good determination of the quartz band, the 464 cm -1 band of an additional quartz standard was used. Both standards were measured several times during the measuring period to correct for potential peak shifts with time. To avoid any heating effects on the carbonaceous material due to the laser, the laser energy on the sample surface was reduced to half of its full power by introducing adequate filters. To account for structural heterogeneity of the carbonaceous material, 15-20 spectra were measured for each sample. The spectra obtained with Raman spectroscopy was processed using the program PeakFit 3. 0 (Jandel Scientific) using Voigt functions.
Acknowledgements We thank  Laura Airaghi  for helpful comments and acknowledge  Ralf Milke ,  Jasper Berndt-Gerdes  and  Anja Maria Schleicher  for electron microprobe and XRF analyses. We also want to thank the reviewers ,  Davide Zanoni , and  Thomas Pettke  for their comments and suffestions to revise this manuscript. This research received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme   FP7/2017-2013/ under  REA  grant agreement no. 604713 , ' Zooming in between Plates (ZIP) '.
The aim of this present study is to provide a first assessment in light of current data collection. Note that we do not assess here the capability of the network to increase our knowledge of the BGC functioning of the Mediterranean. Several papers have already demonstrated the strong potential of the BGC-Argo NAOS Mediterranean array  (Lavigne et al. , 2013 (Lavigne et al. , , 2015;; de Fommervault et al. , 2015a,b; Kessouri et al. , 2017; Mayot et al. , 2017a,b; Testor et al. , 2018). The purpose of this study is more operational: a theoretical design has been implemented and it is timely to assess if it fulfills the initial requirements. Below, Section "The BGC-Argo Floats" outlines the technological enhancements of the BGC-Argo profiling floats, emphasizing their differences with respect to standard Argo floats.
Section "Design of the Array" presents the design of the array and summarizes its achievements in terms of field operations. Section "Results: Assessments of the Array" is dedicated to the different points of assessment. First, metrological verification of the BGC sensors are deployed, strictly according to standard data quality control procedures. Network performances are then presented in terms of spatial and temporal distribution of the data set before assessing whether the array is well dimensioned to provide long-term observation of BGC functioning over the whole of the Mediterranean Sea. Finally, Section "Discussion" reviews the accomplishments, lessons learned, and benefits of the pilot study, with the aim of proposing a revised design for future BGC-Argo arrays.
1 m/s). On this basis, PROVOR-CTS4 provides functionality to set the resolution of vertical sampling of sensors by layers. For the NAOS network, we selected a surface layer (0-350 m), at the resolution of 1 m, and a deep layer (350-1000 m) at 10 m resolution. For the CTD sensors, vertical resolution is the same as for the BGC sensors. CTD sensors of the NAOS BGC-Argo network are calibrated and processed following the procedure described in  Taillandier et al. (2018). For nitrate sensors, the vertical resolution is different: 5 m for the surface layer (0-250 m) and 20 m for the deep layer (250-1000 m). Limitations due to the size of data sets transmitted to land have been overcome thank to Iridium RUDICS stream flow. A PROVOR-CTS4 mission consists in a succession of cycles. Each cycle is composed of three phases.
Indeed, while satellite series are calculated from the one database displaying high homogeneity, the BGC-Argo-derived series are calculated by merging profiles from various individual missions. Additionally, BGC-Argo series are generated with profiles obtained on different timestamps, with different coverage depending on the year. Simple normalization by the maxima (as in D'Ortenzio and Ribera d'Alcalà, 2009) is liable to introduce an important bias to the final series (for example, in case of maximum values derived from data sampled only by a single float or during a particular event). To minimize these effects, normalization, here, was based on the range of variability between the value of absolute maxima and minima of each series (i. e. , maximum value is fixed to 1 and minimum value to 0, with other values transformed accordingly): CHL norm = (CHL -CHL min ) (CHL max -CHL min ) For the sake of consistency, the same transformation was applied to the satellite series (Figure  6 ).
The values used to normalize the time series are shown in Table  4. As a first quantitative assessment, a Pearson's chi-squared test was applied to each bioregion to evaluate for similarity between the climatological cycles of the NAOS array and of the satellite ocean color (Table  4 ). The Pearson's chisquared test (defined by  Pearson (1900)  and implemented here with the R function "cor") provides an estimation of correlation between two time series. It provides a% value, which indicates the degree of similarity between two time series (100% means that a perfect linear relationship exists between the two data sets). Overall, we obtained high values when applying the test, the largest differences appearing for bioregions 4 and 5. The CHL values normalizing the time series were different for satellite and BGC-Argo data.
Generally, satellite values were higher than the BGC estimates. An exhaustive explanation of these differences would require a specific matchup analysis, performed by spatiotemporally co-locating BGC-Argo estimations with satellite observations, as generally performed in ocean color cal/val analysis (i. e. ,  Werdell and Bailey, 2005). Direct comparison of BGC-Argo data with concurrent in situ HPLC estimations (shown in section "Metrological Verification of the Sensors") indicates that BGC-Argo CHL estimations are relatively accurate in the Mediterranean Sea. We thus suppose that the differences between satellite and BGC-Argo estimates could be partially ascribed to the well-known overestimation of the standard ocean-color Nasa algorithm of the Mediterranean waters  (Volpe et al. , 2007) , or to the different characteristics of databases used to generate the time series (i.
e. , different years, different resolution, different data density). We introduced normalization of the time series specifically to minimize such incoherence and to focalize evaluation of the BGC-Argo network more on the temporal shape of the time series than on absolute values. Despite differences in the absolute values applied for normalization, comparison of the satellite and BGC-Argo time series shows that the general characteristics of the phenological series, as already identified by D'Ortenzio and Ribera d'Alcalà (i. e. , two main typologies of series, bloom and no bloom, plus an intermediate one which combines the two characteristics) are well captured by the BGC-Argo series. Overall, the shapes of the time series are similar for the two databases, although some differences can be observed.
For bioregions 1-2-3 (no bloom), the periods of enhancement (November, December, January, February) and those with very low values (June, July, August) coincide in the two databases. The BGC-Argo series show a delay in winter enhancement of approximately 1 month compared to the satellite series. In addition, still for bioregions 1-2-3, the winter enhancement appears relatively flat in the BGC-Argo series whereas the annual maxima are more pronounced in the satellite time series. For bioregion 4 (intermediate), the absolute maxima of the BGC-Argo series are more pronounced than those obtained from satellite measurements. The two series exhibit a maximum at the same periods (mid-March) and their periods of increase/decrease are coincident. Bioregion 5, also, gives rise to similar characteristics in the two databases, showing for both a short and intense enhancement in spring and low values for the rest of the year.
The dates of the peaks, however, are different in the two series (March for satellite, April for BGC-Argo). In conclusion, the assessment of the BGC-Argo network using satellite phenology as a comparison metric is largely but not entirely satisfactory. The BGC-Argo data are able to reconstruct most of the general characteristics of the Mediterranean phenology as inferred from satellite ocean color. Overall, the shapes of the seasonal cycles are similar in both databases. Some significant differences exist, however, in particular the 1month delay for spring decreases in bioregions 1, 2, and 3 and the disparity in dates for the annual maximum in bioregion 5. Different shapes were also obtained for the November-December increases, which appear faster in the satellite than in the BGC-Argo series, regardless of the bioregion.
Distribution of the BGC-Argo Profiles in a Satellite Bioregionalization Framework The initial array design was based heavily on satellite oceancolor products, the only observational database providing a complete and long-term view of Mediterranean BGC dynamics. Following the Argo example, the implementation plan (i. e. , deployment sites, the mission strategy, and the array size) was dimensioned by considering the synthetic view provided by satellite bioregionalization. The array sustained a dozen active BGC-Argo floats and it was assessed as efficient considering the objective to ensure homogenous sampling of each bioregion for at least two seasonal cycles. This a posteriori confirmation of the implementation plan has a twofold consequence. First, although surface (i. e. , satellite bioregions) and deep (i.
ACKNOWLEDGMENTS We thank the PIs of several cruise missions and projects, who enabled the deployment of  BGC-Argo  floats in the Mediterranean:  Pierre-Marie Poulain  and  Giuseppe Civitarese  ( National Institute of Oceanography and Experimental Geophysics, Italy ;  Argo-Italy , cruise ADREX),  Laurent Coppola  ( Laboratoire d'Océanographie de Villefranche, France; LEFE-GMMC ), and  Pierre Testor  ( Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques ) for MOOSE-GE cruises,  Patrizia Ziveri  ( Universiitat Autonoma Barcelona, MEDSEA cruise ),  Mireno Borghin  ( CNR, Italy, Venus2 cruise ),  David Antoine ,  Vincenzo Vellucci ,  Emilie Diamond , and  Melek Golbol  ( Laboratoire d'Oceanographie de Villefranche ,  BOUSSOLE  project). We would also like to thank the entire  NAOS consortium (Serge Le Reste ,  Virginie Thierry ,  Marcel Babin ,  Sylvie Pouliquen  and their respective teams) for their scientific and technical interactions in the last 10 years.
These events correspond to the very tails of the statistical runup height distribution and their analysis requires extremely large datasets. Previous studies have employed different methods to study the statistics of long wave runup, including numerical models, experiments, and field measurements. Theoretically,  [11]  studied the statistical characteristics of long waves on a beach of constant slope using an analytical solution of the nonlinear shallow water theory. The study revealed that the runup height was distributed according to the Rayleigh distribution, if the incident wave elevation was described as having a normal distribution and a narrow-band spectrum. In terms of the statistical moments of the moving shoreline on a beach of constant slope, this study asserts that the kurtosis is positive for weak amplitude waves and negative for strongly nonlinear waves, whereas the skewness is always positive.
1 m, 0. 2 m, 0. 3 m, 0. 4 m, and 0. 5 m) are considered. The calculated time-series for each Hs is 1000 hours (360 000 wave periods). Parallel computations have facilitated the calculation of the statistics of wave runup characteristics for 5000 hours, for each bandwidth, and 10000 hours in total. The numerical computations have been carried out in MATLAB and run on a cluster containing 28 cores. Parameter of the nonlinearity for generated waves is estimated as Hs/h0 and is changing from 0. 03 to 0. 14. The characteristic parameter kh0 = 0. 38 is at the border of validity of the shallow water theory taking into account the horizontal extent of the wave tank. The phase velocity relative error committed by nondispersive theory for kh0 = 0. 38 is only 2. 3%. Thus, at the end of the numerical wave tank the difference between wave crest positions (between dispersive and non-dispersive models) is less than 10%.
Since the focus of this paper is on wave runup, the choice of the theory is justifiable. The choice of wave parameters allows us to see the effects of wave breaking on statistics of their runups. The type of wave breaking is defined by the Iribarren number  [5] : / Ir HL  = , ( 6 ) where H is the wave height, and L is the characteristic wavelength offshore. It is surging or collapsing for Ir ≥ 3. 3, plunging for 0. 5 ≤ Ir ≤ 3. 3, and spilling for Ir ≤ 0. 5. In our dataset, only the first two types of wave breaking, surging or collapsing and plunging, are observed. For Hs/h0 = 0. 03, less than 1% of waves experience plunging breaking, while most of waves are surging. With an increase in Hs/h0 the percentage of plunging waves increases. For Hs/h0 = 0. 06, 32-35% of wave are plunging, for Hs/h0 = 0.
The latter holds for both narrow-banded and wide-banded waves and can be explained by the wave breaking. The corresponding data of wave runup heights are also approximated by a conditional Weibull distribution [Eq. (  11 )], which gives reasonable results and can be used to evaluate the probability of freak runups. Here the threshold s is selected as 0. 7 and the calculated parameters k and λ are given in Table  2. 11 )], fitted to the narrow-band (solid lines) and wide-band (dashed lines) datasets, using the matching colors.
Such powerful VEI 4 events represent only 5 % of the eruptions in the last 10 000 years  (Pyle, 2015) , and curiously two have occurred on Awu with a return period of 154 years. In the database of volcanic eruption victims compiled by  Tanguy et al. (1998) , Awu eruptions claimed a total of 5301 victims, including 963 casualties during the 1812 eruption, 2806 during the 1856 eruption, and 1532 during the 1892 eruption. This latter database did not take into account the 2508 victims of the 1711 eruption  (Van Padan, 1983; Data Dasar Gunung Api, 2011)  and 3200 deaths following the 1822 eruption  (Lagmay et al. , 2007). The latest VEI 4 eruption on Awu in 1966 killed 39 people, injured 2000, and forced the evacuation of 420 000 inhabitants  (Witham, 2005). In total since 1711, Awu's recurrent eruptive activities have caused a cumulative 11 048 fatalities, mainly from lahar events.
2 ) and simultaneously acquired concentrations of H 2 O, CO 2 , SO 2 , H 2 S, and H 2 at 0. 1 Hz. Data were processed using Ratiocalc  (Tamburello, 2015). The scanning differential optical absorption spectrometer (DOAS) was used for the gas emission budget. The instrument performed at a fixed position in the crater (Fig. 2 ). Further details on Awu gas measurements are provided in a separate paper and hereafter referred to as  Bani et al. (2020). During this fieldwork, a less altered rock sample was selected directly on the lava dome and then analyzed for major and trace elements using inductively coupled plasma atomic emission spectroscopy (ICP-AES). The same sample was analyzed in two different laboratories, including Laboratoire Magmas et Volcans (Clermont-Ferrand, France) and Pôle de Spectrométrie Océan (Brest, France).
Acknowledgements. We sincerely acknowledge pak  Endi  and the other staffs of Awu observatory for their support in organizing the expedition to the crater of Awu. We thank  Mhammed Benbakkar  and  Celine Liorzou  for the rock analyses in Laboratoire Magmas et Volcans and  Laboratoire Géosciences Océan , respectively. We greatly appreciate and thank  Corentin Caudron  and  Caroline Bouvet de Maisonneuve  for their detailed review and helpful comments that substantially improved this paper. Financial support. This work was supported by  IRD  under the  JEAI-COMMISSION program  in collaboration with CVGHM. Review statement. This paper was edited by  Giovanni Macedonio  and reviewed by  Corentin Caudron  and  Caroline Bouvet de Maisonneuve.
INTRODUCTION The Triassic Period concluded with one of the most extreme biological upheavals in Earth"s history -the End-Triassic Mass Extinction (ETME, ~201. 6 million years ago  (Blackburn et al. , 2013; Davies et al. , 2017). The ETME constitutes one of the so-called "big 5" Phanerozoic mass extinctions  (Sepkoski, 1982) , ranking in the top 3 for ecologic and taxonomic severity  (McGhee Jr et al. , 2013). In particular, coral reefs and other carbonatedominated ecosystems were preferentially devastated  (Kiessling and Simpson, 2011) , drawing comparisons with the plight of present-day coral ecosystems. The ETME is coincident with the initiation of the rapid emplacement of the Central Atlantic Magmatic Province (CAMP)  (Blackburn et al. , 2013; Yager et al. , 2017) , a large igneous province (LIP) related to the rifting of the supercontinent Pangaea  (Nomade et al.
ACKNOWLEDGMENTS We thank  M. Lewis  and  R. Gallois  for field assistance. This research was supported by the  National Science Foundation  ( NSF Earth Life Transitions Grant   EAR-1338329  and  NSF EAGER 1017536 ),  UK   NERC  grant  NE/H023852/1s  to  S. E. G. , and the  American Philosophical Society Lewis and Clark Fund for Exploration and Field Research in  Astrobiology to Y. I. A. E. T. acknowledges support from  ACS  grant  51182-DNI2 , and  NSF  grants  OCE-1437166 ,  EAR-1542213 ,  EAR-1352212 ,  EAR-1325054 , and  EAR-0949191. V. A. P. and A. E. T. were supported by the  "Laboratoire d"Excellence "  LabexMER  ( ANR-10-LABX-19 ) and co-funded by a grant from the  French government  under the program " Investissements d"Avenir ". R. G. was supported by a  NASA Undergraduate PDS  Fellowship.
Volcanic paleo-surface model The main body of work relates to the topographic reconstruction of a volcano, i. e. the modelling of the surface that it reached after its growth ceased (the so-called pre-erosional volcanic surface, or PEVS). To model such surfaces, we use as input data the sectors of the current topography that still testify to the PEVS or, in other words, that have undergone ideally zero or minimal erosion since the volcano construction ceased. In detail, each grid cell of the present-day DEM was converted in a XYZ point, forming a huge input database (20 million points). Next, different morphometric parameters such as slope, topographic position index  (TPI, Weiss, 2001)  and topographic openness (TO,  Yokoyama et al. , 2002; Favalli and Fornaciai, 2017)  were computed, assigned as attributes for each point and combined to select only points belonging to PEVS.
The elevation versus distance to centre diagram (Fig. 3A ) highlights how CPs, independently of their position along the volcano profile (lower, mid or upper), belong to planezes surfaces, i. e. to the locally (at hundred-meter scale) uppermost surface of the volcano. Note that only the main edifice was selected without considering the volcaniclastic materials of the ring plain immediately surrounding the central edifice  (Zernack et al. , 2011; Németh and Palmer, 2019). Finally, a careful visual control has been carried out in order to remove outlier points accidentally present in the ultimate CPs database. From their respective CPs, numerical reconstructions of 15 from the 16 selected volcanoes have been modelled using the ShapeVolc (SV) algorithm  (Lahitte et al. , 2012). Only Pilişca volcano (a small-sized shield volcano topped by a few lava domes in South Harghita;  Szakács and Seghedi, 1995)  has been reconstructed by the Kriging interpolation  (Krige, 1951).
Volumetric results Using the output from the geomorphological models, the main individual data for each volcano are compiled in Table  2  and comprise the area (A), Height, constructed (VC) and eroded (VE) volumes, growth (GR) and erosion (ER) rates and the degree of denudation (DDen). To compute GR and ER, we considered all available radiometric ages from previous studies, most remaining conventional K-Ar ages being obtained on whole rock. For each volcano, we calculated an average age using as bracketing temporal constraints the oldest and youngest available ages and assuming that the average age represents the peak of activity. The VC computed for the 16 volcanoes modelled in this study ranges between 3±3 and 592±190 km 3. Fitting the volcano volume and area by a power-law function shows a good positive correlation (R 2 =0.
Nonetheless, even if the amount of erosion (VE) seems to be nicely correlated to VC, it is less relevant than the correlation between DDen and the time (dashed line in Fig. 5B ). By averaging DDen values (Table  2  and black dots in Fig. 12 ) on each CGH segment leads to the following DDen values: 30%, 25%, 20% and 15% (bottom of Table  2 ), confirming the progressive degradation of volcanoes toward north, as first quantified by  (Karátson, 1996). Correlation of the DDen versus time allows establishing relationships between erosion rate (ER, i. e. aerial denudation or lowering of edifice height) and time. To better constrain the fast evolution of DDen for the early stage of dismantling, we added to the CGH volcanoes dataset (this work, black dots in Fig. 12 ) the values proposed for the dozen edifices of the Ciomadul volcanic complex dating to between 0.
Acknowledgments We are particularly grateful to the two anonymous reviewers and  Karoly Nemeth  as well as Editor  Markus Stoffel  for their comments and suggestions, which greatly helped us to improve the clarity of this paper. This research has been supported by the  Hungarian Scientific Research Fund NKFIH-OTKA  No. 765 K 115472  to DK. Financial support for SD and PL was also provided by the  SYSTER 2017 program 766 of INSU, CNRS. This is  Laboratoire de Géochronologie Multi-Techniques  (LGMT) contribution number  155.
Material and Methods All data presented in this study were obtained during the oceanic campaign SAMOA-SPT from August 27 th to September 10 th 2015 aboard R/V Alis  21. An extensive 2D high-resolution seismic survey was carried out, along with 1 m-resolution bathymetric and seafloor reflectivity surveys. A Seistec-IKB boomer was used for the seismic acquisition, with a vertical resolution of approximately 25 cm allowed by its 1 to 10 kHz bandwidth. All raw profiles were processed using iXBlue DELPH seismic acquisition software. A frequency filter was applied between 900 Hz and 10 500 Hz, coupled with a linear AGC (Amplitude Gain Control) and stacking of three adjacent traces. A Kongsberg EM-1002 multibeam sounder was used for the bathymetric and reflectivity acquisition with a 95 kHz frequency.