dataesr/scientific-paragraphs-categorization huggingface dataset:
Whole-rock major and trace element geochemical compositions were analyzed at the laboratory of the Mining and Geological Survey of Hungary (Budapest, Hungary). Major elements were determined by ICP-OES, whereas trace elements were analyzed by ICP-MS following a lithium borate fusion and dilution in acid. Detection limits vary between 0. 02 and 0. 15 wt% for major elements and 0. 05-1 ppm for of trace elements. Results of the analyses are presented in the Supplementary Material. In the case of Kožuf-Voras, we combined our results of Šumovit Greben with previously published whole-rock geochemical data and classified the volcanic centers of the whole system based on their K 2 O and SiO 2 composition. The statistical analysis was done using Hierarchical Cluster Analysis (HCA) in IBM SPSS Statistics 23, on normalized values using Ward's method and Euclidian distances.
5 ). Compiling the present geochemical dataset with the previously reported one in case of the HMg-K and the UK centers  (Cvetković et al. 2004; Yanev et al. 2008a, b) , while there are similarities in the major and trace element compositions in general, differences exist in their Sr and Nd isotopic ratios and in some of the trace element content (e. g. LILE; Fig. 5 ). Yanev et al. (2008a, b)  concluded that the HMg-K and the UK rocks were not affected by crustal contamination processes but their geochemical characteristics reflect a metasomatized lithospheric mantle, and the differences within the samples imply source heterogeneity. Debrište samples fit also into this interpretation, representing the lowest 87 Sr/ 86 Sr and highest 143 Nd/ 144 Nd isotope ratios, whereas the isotopic composition of Pakoševo might not reflect its original source region as discussed above.
' ICER ' project, and co-financed by the  French-Hungarian Cooperation Program   TÉT-FR-2018-00018. Fieldwork was carried out under Research Permit No. UP1-11/1-690/2019  issued by the  Ministry of Environment and Physical Planning of the Republic of North Macedonia. The SEM measurements were financed by the  GINOP-2. 3. 3-15-2016-00029 ' HSLab ' project. Financial support for sampling and K-Ar dating was also provided by the  TelluS 2020 program  of  INSU, CNRS. This is  Laboratoire de Géochronologie Multi-Techniques  (LGMT) contribution number  168. K. Molnár  benefitted  of Trans National Access program  of  EU-HORIZON 2020  funded  EPOS  project  N 676564 for Sr-Nd  isotope analyses, which were also partially supported by  IGG-CNR  Grant  P0CT0061  by  S. Agostini. The authors thank  Wolf-Christian Dullo  for the editorial handling of the manuscript and  Georgia Pe-Piper ,  Albrecht von Quadt  and an anonymous reviewer for their constructive comments and suggestions.
Whether or not it should be modified merits further discussion. More important is that the community move forward as a whole to go beyond reporting changes in pH alone, thereby avoiding the unwitting focus on relative rather than absolute changes in hydrogen ion concentration. by the US National Science Foundation (grant no. OCE 1657799), and James C. Orr by the French ANR Project SOBUMS (grant no. ANR-16-CE01-0014) and EU H2020 Project COMFORT (grant no. 820989). Review statement. This paper was edited by Peter Landschützer and reviewed by two anonymous referees.
Acknowledgements. We thank  Stephen Gonski  and two anonymous reviewers for their helpful comments. Financial support. Andrea J. Fassbender  was supported by the  David and Lucile Packard Foundation/MBARI ,  Andrew G. Dickson  https://doi. org/10. 5194/bg-18-1407-2021Biogeosciences, 18, 1407-1415, 2021.
Data availability. All data used in this analysis are publicly accessible, and the appropriate references have been provided. Specifically, Fig. 2  was made using information from  Sutton et al. (2014)  and  Bates et al. (2014)  and the data products described in Fassbender et al. (2017), which can be accessed at https://www. socat. info/index. php/products-using-socat/, last access: 23 Septem-ber 2020. GFDL ESM2M data used to make Fig. 3  can be accessed at ftp://nomads. gfdl. noaa. gov/CMIP5/output1/NOAA-GFDL/, last access: 23 September 2020. The gridded GLODAPv2. 2016b dataset used to make Fig. 4  can be accessed at https://www. ncei. noaa. gov/access/ocean-carbon-data-system/oceans/GLODAPv2/, last access: 23 September 2020. Supplement. The supplement related to this article is available online at: https://doi.
Seismicity of Swarm-2 The swarm-2 analysis was based on hydroacoustic records from only seven AuHs (MAD-E and WKER AuHs recorded no data in the period of interest) and from 25 September to 27 October; this analysis led to the location of 5099 events. This number decreased to 5031 after selecting events with origin time-errors less than 10 s. We then removed all events within 40 s of the preceding one, yielding a catalogue of 4954 events (compared with 92 events in the ISC catalogue). After re-picking this first catalogue, 4880 events were relocated of which 782 events (16%) were located using four hydrophones, 2997 events (61. 5%) using five, 1039 events (21%) using six, and 62 events (1. 5%) using seven hydrophones. Only 2999 events were discernible in the S-SWIR records because of the high seasonal noise in the data and only 2039 events in ELAN data due to the fact of its remoteness from the swarm (~2500 km).
Conclusions Due to the remoteness of the Southwest Indian Ridge, most of its seismicity is known from teleseismic records over several decades but limited to magnitudes larger than mb = 4. 5. In situ seismicity recordings with ocean bottom seismometers are rare and limited in time (e. g. ,  [7, 41] ). Long-term seismic monitoring with autonomous hydrophone networks bridges this gap by capturing lower magnitude (down to mb = 3. 2) and transient events. Here, we analyzed the hydroacoustic activity associated with two swarms that occurred in 2018 along the SWIR, using data from the OHASISBIO hydrophone array. These two swarms occurred in contrasting tectonic contexts: swarm-1 on SWIR Segment 18, bounded by two large offset transform faults (Atlantis II and Novara FZ), swarm-2 on SWIR Segment 4, at a ridge discontinuity 200 km from the Rodrigues Triple Junction.
INSU-CNRS provided additional support and the Regional Council of Brittany (CPER) funded the hydrophone moorings. V. V. I. was supported by a fellowship from the University of Brest and from the Regional Council of Brittany, through the ISblue project, Interdisciplinary Graduate School for the Blue Planet, co-funded by ANR (ANR-17-EURE-0015) and by the French government under the program "Investissements d'Avenir".
The resistive wall and the free-boundary conditions are included by coupling JOREK to the STARWALL code  [14, 16, 21]. The coupling is performed by solving the full vacuum domain with a Green's function method and therefore the vacuum does not need to be meshed. STARWALL uses the thin wall assumption and discretises the wall with linear triangular elements. Although the employed wall formalism is implemented for 3D thin walls including holes, the used setup is restricted to an axisymmetric wall.
Acknowledgements This work was supported by the  ITER Monaco Fellowship Programme. The authors acknowledge access to the  EUROfusion High Performance Computer (Marconi-Fusion)  through  EUROfusion  funding to perform the presented simulations. ITER is the  Nuclear Facility INB  no. 174. This paper explores physics processes during the plasma operation of the tokamak when disruptions take place; nevertheless the nuclear operator is not constrained by the results presented here. The views and opinions expressed herein do not necessarily reflect those of the  ITER Organization. This work was supported by  US DOE  Contracts No. DE-AC02-09CH11466  and No. DE-SC0018001 , and the  SciDAC Center for Tokamak Transient Simulations. Some of the computations presented in this report used resources of the  National Energy Research Scientific Computing Center (NERSC) , a  U.
S. Department of Energy Office of Science User Facility  operated under Contract No. DE-AC02-05CH11231. The support from the  EUROfusion Researcher Fellowship programme  under the task agreement  WP19-20-ERG-DIFFER/Krebs  is gratefully acknowledged. Part of this work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme 2014-2018 and  2019-2020  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. The authors would like to thank  Guido Huijsmans  and  Michael Lehnen  for fruitful discussions about the contents of this article.
Each eruption cycle is characterized by erupted volumes ranging from ~0. 15 to ~15 × 10 6 m 3 and peak TADRs between 0. 35 to 52 m 3 s -1 (Table  1b ). The average duration of each eruptive cycle is 26. 7 (± 20. 7; 1σ) days, much shorter than the average inter-eruption time of 222. 7 days. The cumulative volume curve of Bezymianny is essentially controlled by the sudden steps associated with the eruptive cycles detected by MODIS (Fig. 3c ). Between 2000 and mid-2002, eruptions reached higher TADR peaks, causing a steeper cumulative volume curve than in the rest of the time series (Fig. 3c ). Although this may reflect a higher magma output rate in this period, it is also possible that the dataset is biased by the fact that only one satellite was operating in that period (see Methods). Similar to Klyuchevskoy, the cumulative curve of the volumes erupted between May 2002 and September 2012 suggests steady-state volcanism for Bezymianny  61  , which is characterized by an average output rate of ~0.
5) for Bezymianny's activity until 2012 (Fig. 4b ), which is completely lost when including the whole dataset (Fig. 4c ). No correlation is found between the intereruption times and volumes released during the last or the next eruption (Fig. 4 ). Although the lack of correlation can be due to the significant uncertainties affecting the Bezymianny time series (Methods), we may not exclude the role of a time-varying upper threshold (strength) of the shallow magmatic system  65. Bezymianny. The variation from the steady-state is calculated as the residual between the linear growth model and the observed cumulative volume curves. A two-fold standard deviation (± 2σ) of residual is used to define the upper and lower volume limits for the load-discharge model. In this plot, a time-predictable system would have a near-constant upper threshold of the volume load when eruptions occur.
Satellite Thermal data Satellite thermal data were processed using the MIROVA system 60 (www. mirovaweb. it), which is based on the analysis of the images acquired by MODIS. The two MODIS sensors, launched in March 2000 and May 2002, provide approximately six infrared images per day over Kamchatka (three night-times and three daytimes) with a nominal ground resolution of 1 km. MODIS images are processed at each volcano to quantify the Volcanic Radiative Power (VRP in Watts), a combined measurement of the area and integrated temperature of the hot (>200°C) volcanic features with a standard error of ± 30% over every measurement  60. We used only the night-time MODIS dataset, consisting of approximately 19,500 images acquired over the Klyuchevskoy Volcanic Group (KVG). Thermal anomalies detected by MIROVA were geolocated (errors in geolocation are less than 0.
5 km for nadir acquisition  60  ) to discriminate the hotspots sourced by the three distinct volcanoes. All the images were visually analyzed to discard the data contaminated by clouds, ash plumes, or poor viewing conditions (i. e. , high satellite zenith), which preclude a correct estimation of VRP  15, 52. Finally, the supervised dataset consists of 2,139 images for Klyuchevskoy, 2013 images for Bezymianny, and 219 images for Tolbachik, which have been used to reconstruct the time-series of VRP (Fig. S1  -Supplementary Material). For each volcano, the cumulative Volcanic Radiative Energy (VRE) in Joules is calculated as the trapezoidal integration of the supervised VRP time series (Fig. S1  -Supplementary Material).
Statistical testing of correlated activity The frequency plot of inter-eruption time (dt_es in Tab 1) for Bezymianny and Klyuchevskoy is shown in the left axis of Fig. 4. The peaked distribution for Bezymianny data (gray bars in Fig. 4a, b ) can be reasonably fitted by a Brownian-Passage Time (BPT) distribution (blue line). This models assumes a fixed eruption threshold and volume release, plus a constant loading rate with noise. The coefficient of variation (CV), also called the aperiodicity parameter, measures a signal's periodicity, where CV=0 refers to a perfect periodicity, CV=1 to a random Poisson occurrence, and CV>1 to clustering. When considering the whole dataset of Bezymianny (Fig. 4a ), the CV value is 1. 20, indicating a random occurrence of eruptions. However, the CV value decreases to 0.
Acknowledgments This research was funded by the  Italian Ministry for Universities and Research (MIUR). Part of this study was supported by the  European Research Council  under the  European Union   Horizon 2020 research and innovation program  (Grant Agreement 787399-SEISMAZE) and by the  Russian Ministry of Education and Science  (Grant  14. W03. 31. 0033 ). We acknowledge the LANCE-MODIS system (http://lancemodis. eosdis. nasa. gov/)for providing Level 1B MODIS data.
Acknowledgements RCK grateful acknowledges the financial support of  AVRUL  ( Agence pour la Valorisation de la Recherche Universitaire du Limousin ) for his stay in IRCER, UMRCNRS 7315, Limoges, France under 2018  AVRUL postgraduate internship. The authors also like to express gratitude to the Director of IRCER for having made accessible all accommodations for carrying out this work in the laboratory. This project received the contribution of the  FLAIR fellowship African Academic of Science  and the  Royal Society  through the funding  N° FLR/R1/201402. The participation of ASY and JLP has been supported by  EPSRC  through grant  EP/T013524/1.
Laboratory Analysis on sediment cores X-ray images of the sediment cores were obtained with a Fuji PRIMA T2 digital acquisition system from Ibis X-rays systems and a Gilardoni cpx-m 160 generator. Magnetic susceptibility was acquired with a Bar-tington® system mounting an MS2 sensor (range 0. 1 SI) and using a 2 cm spacing resolution. X-ray fluorescence (XRF) data were acquired with a 3rd generation AVAATECH core scanner on split cores equilibrated for room temperature, carefully smoothened to remove surface roughness and covered with a 4 μm Ultralene® film to prevent contamination of the sensor during core logging. The generator is an Oxford 100-W X-Ray source with rhodium anode, while the sensor is a Canberra Silicon Drift Detector with Be window. The XRF data were collected in three separate runs using generator settings: 10 kV with helium flux, 30 kV, 50 kV and 400-450 μA with counting times of 10-35 s every centimetre downcore.
Dead times were automatically corrected. Raw spectroscopic data were processed and converted to elemental counts by Iterative Least square software (WIN AXIL) package from Canberra Eurisys. Post-processing of the results indicated that light elements (Al to Fe) were affected by seawater content, with Cl values showing an opposite trend. Thus, light elements from lithogenic sources were replaced by heavier elements when normalizing XRF data (e. g. Fe replaced by Ti)  (Hennekam and De Lange, 2012; Tjallingii et al. , 2007). Foraminifera assemblages were examined by means of an optical stereomicroscope to obtain a semi-quantitative micropaleontological analysis on 1 cm thick sediment slices taken every ~10 cm. The sediment samples were dried in oven at 50 • C and sieved at 0. 063 mm in advance of the micropaleontology analysis.
Radiocarbon AMS dating were performed on monospecific samples (Table  1 ), when possible, of planktonic or benthonic foraminifera at the National Ocean Sciences Accelerator Mass Spectrometry Facility (NOSAMS, Woods Hole Oceanographic Institution, USA). Specimens were picked-up under the optical stereomicroscope from the size fraction >0. 180 mm, discarding filled, encrusted and broken specimens. The selected specimens underwent an ultrasonic bath in demineralized water before shipping to the laboratory. The calibration was obtained with the Calib 7. 1. 0 Radiocarbon Calibration Program  (Stuiver and Reimer, 1993)  and the Marine13 calibration data set  (Reimer et al. , 2013)  applying a reservoir age of 71 ± 50 years according to the Calib database for Sicily  (Kuhlmann et al. , 2015; Siani et al.
The stratigraphic position of the main foraminiferal bioevents is reported. The reference INTIMATE event stratigraphy is from  Björck et al. (1998)  and  Blockley et al. (2012). The ages reported for the reference core MD04-2797CQ are calibrated (yr BP) according to  Rouis-Zargouni et al. (2010). The data were not corrected for the ice volume effect. G. r. = Globigerinoides ruber, G. s. = Globorotalia scitula, G. tr. rc = Globorotalia truncatulinoides right coiling, GS = Greenland Stadial, GI = Greenland interstadial, HS = Heinrich Stadial, LGM = Last Glacial Maximum chronozone according to  Mix et al. (2001). B) Micrographs of foraminifera assemblages and their ecozones: a & b) hemipelagic sample without terrigeneous; c) foraminifera with terrigenous; d) cold-water planktonic foraminifera showing different test sizes (no hydraulic selection); e) abundant vegetal debris with fine terrigenous; f) foraminifera assemblage, including taxa typical of inner shelf (Elphidium crispum), with fine terrigenous.
Rouis-Zargouni et al. (2010)80±4014 C age) byMinisini et al. (2007), at 2500 yr BP by interpolation byRouis- Zargouni et al. (2010)andDesprat et al. (2013), and at 3600 yr BP (4139 ± 37 14 C age) byFerraro et al. (2018). The discrepancy between the calibrated ages byMinisini et al. (2007)andFerraro et al. (2018)depends on the applied reservoir age (ΔR value of 71 ± 50 yr byMinisini et al. , 2007and no ΔR correction byFerraro et al. , 2018), as the radiocarbon ages are very close. Here we preferred the calibrated age byMinisini et al. (2007), as based on the reservoir correction available for Strait of Sicily in the Calib dataset, while we rejected the much younger age byRouis-Zargouni et al. (2010)confirming the age uncertainties for the upper 80 cm of core MD04-2797CQ suggested byDesprat et al.
ACKNOWLEDGEMENTS The authors would like to thank  Sebastian Tappe  and two anonymous reviewers for their time and the quality of their critical comments that helped us improving the initial version of our manuscript, as well as  Jeffrey G. Catalano  and  Rajdeep Dasgupta  for the editorial handling. We are grateful to  Catherine Zimmerman ,  Aimeryc Schumacher ,  Laurie Reisberg ,  Christiane Parmentier  and  Damien Cividini  for their help in the clean lab and radiogenic isotope analyses at CRPG. Equally, we would like to thank  Pierre Cartigny  for granting us full access to the fluorination line and gas source mass-spectrometer for S-isotope measurements at IPGP while the CRPG fluorination line was not installed ''yet". This work benefitted from the financial support of  Agence Nationale de la Recherche  to  ET (ANR Dear-Sir ;  ANR-15-CE31-0005 ).
The barite has a higher content in total REE and contains non-saline fluid inclusions on the graben shoulders, suggesting that fluid circulations within the border faults interact with sulphate rich layers and precipitate at temperature above 150 °C. In deepseated samples from the central part of the basin, barite fluid inclusions show a wide range of salinities, suggesting a higher contribution of sedimentary brines and precipitation at lower temperatures (< 150 °C). According to their REE signature, these barite mineralisations are associated with siderite and apatite with a diagenetic source. A conceptual model for fluid circulation within the basin is built from this new dataset. Fast and deep down-and up-flows occur along the major border faults, locally leaching evaporitic horizons. A part of the infiltrated meteoric waters reaches the centre of the basin, where it then mixes with the brines in sedimentary.
Material and methods Few datasets with a detailed structural background are available on the western shoulder and deep-seated of fracture mineralisations in the Buntsandstein Gp. , despite their contribution to fracture permeability, especially regarding barite  (Griffiths et al. , 2016). A multi-disciplinary approach is developed to determine the source of fluids causing mineralisation within the fractures and filling the matrix. N000-N010°E N040°E N140-N170°E B002 B003 B005 B006 B007 X X X X X X X Marlenheim URG western shoulders Upper Buntsandstein þ120 Outcrop 48. 624017N 7. 474710E Barite N000-N010°E N040°E N140-N170°E X009-BL X009-OR X015 X020 X022 M022 X023 X025 X026 X029 X X X X X X X X X X X X X X X X X X X La Fonderie Vosges granitic basement þ300 Outcrop 47. 838175N 6. 761800E Granite (Carboniferous) N095°E LFX005 X X Lipsheim Deep-seated reservoirs Upper Buntsandstein À1500 (precise depths for each sample is confidential) Borehole Core 48.
Two populations of inclusions emerge from the overall dataset. The first population with high T h ranging from 180 to 240 °C, with very low saline to non-saline fluids, differs from the second population of milder T h ranging from 80 to 150 °C, and with varying salinity, from very low salinity to high salinity. For the second population, the colder the T h is, the higher is the salinity. The gaseous phases of inclusions within barite were also analysed with Raman spectrometry. H 2 O mainly composes the gas phases trapped. Neither CO 2 nor H 2 S were detected. The samples from deeply seated fractured sandstones show mostly inclusions from the second family, whereas the samples from the URG shoulders have inclusions belonging to the first described family of inclusions. The sample from Roemerberg (C2335) also shows primary and pseudo-secondary oil trapping inclusions and inclusions plans, with T h from 100 to 120 °C, av.
Acknowledgements. We want to thank the geoscience team from  ENGIE Deutschland GmbH (now Neptune Energy Germany ) and their partner  Palatina GeoCon  to supply core material, scientific support, and rich debates. All the  GeoRessources laboratory, Neptune Energy  and ENGIE collaborators are thanked for numerous advices, settings supplies and help with measurements, the help and experience of the collaborators from the  CRPG laboratory (Nancy). We would like to thank the  ChronoEnvironnement laboratory in Besançon , for the supply of cold cathode facility, and discussions on diagenesis of fractured sandstones. We are grateful to the editor and the reviewers for their time and suggestions for improving the manuscript.
A T was determined potentiometrically using a Metrohm© titrator (Titrando 888) and a glass electrode (Metrohm©, Ecotrode Plus) calibrated using first NBS buffers (pH 4. 0 and pH 7. 0, to check that the slope was Nernstian) and then using a TRIS buffer solution (salinity 35, provided by Andrew Dickson, Scripps Institution of Oceanography, USA). Triplicate titrations were performed on 50 mL sub-samples at 25 • C, and A T was calculated as described by  Dickson et al. (2007). Titrations of standard seawater provided by Andrew Dickson (Scripps Institution of Oceanography, USA; batch 151) yielded A T values within 5 µmol kg -1 of the nominal value and a standard deviation of 1. 5 µmol kg -1 (n = 40). All parameters of the carbonate chemistry were determined from pH T , A T , temperature, salinity, and phosphate and silicate concentrations using the R package seacarb.