dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016 , White et al. , 2014]  and (iii) the development of softwares using these multiple databases to model the mineralogical evolution of a rock from its chemical composition. This led to the development of modelled phase diagrams (often called "pseudosections"), as THERMOCALC  [Powell et al. , 1998 ] PERPLE_X  [Connolly, 2005] , THERIAK-DOMINO  [De Capitani and Petrakakis, 2010] , BINGO-ANTIDOTE  [Duesterhoeft and Lanari, 2020]  and GeoPS  [Xiang and Connolly, 2022]  are currently used. Pseudosections allow the effects of major and minor elements, of the Feoxidation state and/or of the amount and nature of fluids on P -T stability fields of mineral assemblages to be explored. These models allow also the mode and the composition of minerals to be determined for a given rock composition under specific P -T conditions  [Lanari and Duesterhoeft, 2019, Yakymchuk, 2017].
, 2017, Tsujimori et al. , 2006, and Figure   5 ]. The "classical" P -T domain of the "Franciscan" metamorphic gradient must be widened to both high pressures and low temperatures. Thus, the lawsonite-bearing eclogites from Guatemala are close to the famous "forbidden zone" of  Liou et al. [2000] , i. e. a geotherm of ∼5 °C•km -1. The division proposed by  Cloos [1993]  deserves to be questioned, as in a given subduction context, both hot and cold metamorphic paleo-gradients can develop simultaneously. Thereby, the data obtained in eastern Cuba and Guatemala show that within the same tectonic unit, eclogites outcropping close to each other recorded contrasting P -T conditions. This dataset is in line with the thermo-mechanical models of subduction channels. Indeed, since Shreve and  Cloos [1986]  and  Cloos and Shreve [1988] , numerous models of a specific layer of low viscosity rocks dragged by the subducted plate beneath the upper plate have been developed  [Gerya et al.
Acknowledgements This study was supported by the  Service National d'Observation en Volcanologie  (FD, DJ), the  Tellus-Syster program  (DJ) by the  Institut National des Sciences de l'Univers of the French Centre National de la Recherche Scientifique (CNRS-INSU)  and the  InVolc Graduate Track program  of  Université Clermont Auvergne. The authors are deeply grateful to the  OVSG (Observatoire Volcanologique et Sismologique de Guadeloupe)  staff for logistic support and scientific discussion. We further thank  Claude Hervier  at the  Observatoire de Physique du Globe de   Clermont-Ferrand (OPGC)  for maintaining and calibrating the high-impedance voltmeter used for SP measurements,  Tristan Didier  for producing the thermal probes and  Tommaso Boni  for great support in the field. This is contribution no.
Whenever they are available, well-dated and continuous continental and marine records can shed light on the chronology of climatic events which punctuated MIS 3. However, these records are too scarce to provide a global view of the evolution of climatic and environmental conditions over North Africa and the Arabian Peninsula. In particular, they cannot address the geographical spreading and retraction of humid areas. Thus, to complement these few continuous records, we also collected published data to build a comprehensive database of well-dated samples from wet environments (lakes, paleosoils, river terraces, concretions and travertines). We interpret the number of dated samples over 1000-year time windows along the MIS 3 as an indicator of humidity. The likelihood of collecting and dating those wet paleo-environmental archives is higher for time intervals when humid conditions prevailed over North Africa and the Arabian Peninsula, thus resulting in many more sedimentation sites and higher chances that some of these archives have reached us despite the intense deflation of the last ice age.
The data sets In order to subdivide our dataset into internally consistent, climate areas, three main regions have been distinguished based on the respective influence of tropical and Mediterranean climates. These three regions are: (1) Tropical Africa (including the Eastern Atlantic) from the Atlantic coast to the eastern border of Sudan which encompass areas under the predominant influence of summer rains from the African monsoon, (2) North Africa influenced by winter rains from the Mediterranean area and (3) eastern tropical Africa (Ethiopia and Dibouti) and the Arabian Peninsula (including the Arabian Sea and the Red Sea), which are influenced by the Indian monsoon, and the Mediterranean rains. Marine records from the eastern Mediterranean are included in sector 3 because they are influenced by the Nile River, whose watershed includes the Ethiopian highlands.
Tropical Africa, including eastern Atlantic Ocean sites We selected long, published sequences from West Africa to provide us with vegetation information (pollen, plant waxes, carbon isotopes) and continental hydrology (pollen, sediments). From South to North (Figure  2 ): • Gulf of Guinea: Pollen data were used to reconstruct phases of forest expansions in cores GIK 16856-2 (4. 805 N, 3. 40167 E)  [Dupont and Weinelt, 1996]  and Bambili (5. 93369 N, 10. 24064 E)  [Lézine et al. , 2019]  located off the mouth of the Niger River and off the nearby Cameroon highlands, respectively. • Off the Sahel and the Sahara: The percentage of C 4 and C 3 vegetation as well as variations in continental humidity were reconstructed from (i) marine records of the δ 13 C of plant leaf waxes (n-alkanes) obtained on core  GeoB9528-3 [9.
Continental hydrological samples Our data set contains 245 dated samples from lake sediments, paleosoils, fluvial terraces, speleothems and travertines/springs collected at 82 localities in North Africa and the Arabian Peninsula and published in the litterature (Figures  1  and 4 ; Supplementary Table  1 ). We also examined seventy-four dated groundwater samples from 27 localities. The age control is based on (i) 75 AMS and conventional radiocarbon dates and Electron Spin Resonance dates (ESR) on lake carbonates, remains of aquatic faunas and organic matter (ii) 61 luminescence (OSL, TL, IRSL) dates on quartz grains and (iii) 46 Uranium-Thorium (U-Th) dates on lake, spring and speleothem carbonates. All dates are abbreviated ka BP. Raw 14 C dates were converted to calendar ages using the CALIB 8.
The method was first applied to the Wadi Shati lacustrine deposits (Libya) by  Gaven et al. [1981]. In addition,  Rogerson et al. [2019]  provided an extremely detailed chronology of MIS 3 from 35 U/Th measurements of speleothems in Susah Cave, northern Lybia. We also put in our database the ESR dates from the Kharga Oasis (Egypt) obtained on fresh-water gastropod shells  [Blackwell et al. , 2012]. In addition to the chronological control points listed above, we also collected 14 C and U/Th measurements performed on African and Arabian groundwaters falling within MIS 3. However, the groundwater ages can be biased by the mixing of groundwaters from different layers in the stratified aquifers or the contributions of ancient carbon through the dissolution of carbonate minerals or the oxidation of old organic matter in the soil  [Froehlich et al.
Acknowledgements We would like to thank the editors of this special volume in honour of  Professor J. Dercourt  for inviting us to contribute. The computing time was provided by  GENCI  ( Grand Equipement National de Calcul Intensif)  and the simulations were performed using  Curie  at  TGCC (Très Grand Centre de Calcul du CEA). M. -N. Woillez  and  P. Le Mézo  are thanked for taking part in this endeavour, as part of their post-doctoral and PhD projects, respectively. The MIS 3 60 ka simulation was initially run for the  European Research Council TracSymbols  project led by  C. Henshilwood  and  F. D'Errico  (Grant no. 249587 ).
Vegetation data collection From 2010 to 2013, Tour du Valat Research Institute (TDV) had inventoried 8 % of the surface of the site following the Natura 2000 nomenclature to delimit habitats with polygons on a vector layer (1/ 10000 scale) using field knowledge and photointerpretation of aerial photographs acquired on 04/05/2011 by the French National Institute of Geographic and Forest Information. While Natura 2000 habitat typology provides valuable background information, it is not accurate enough for satellite-based mapping  (Vanden Borre et al. , 2011). Hence, in 2016 we performed a field campaign adapted to remote sensing analysis. We identified homogenous stands of halophilous scrubs and other land cover types distributed over the entire study area. Two hardto-reach zones were surveyed with a drone (Phantom 4 -DJI) equipped with a visible camera (CMOS 1) to locate homogeneous stands of vegetation on newly formed dunes.
The sample totalled 2011 points (Table  A. 1) with 76 points of A. macrostachyum and 84 points of S. fruticosa. For each sampling point located with its GPS position, the corresponding pixel value was extracted for each spectral band using the point sampling tool plugin in the Quantum GIS software (QGIS). Using the polygons of habitats identified in 2012 and 2013, we extracted vegetation points separated by 2 m from each other and placed at a minimum distance of 5 m from the border of the polygon. These points were used to test the accuracy of the final map of halophilous scrubs on an independent sample. A total of 2653 points with 162 A. macrostachyum and 131 S. fruticosa (Table  A. 2) were established. Field reflectance measurements were also collected in September 2016 using a JAZ EL 350 VIS-NIR spectrometer (350-1000 nm) from Ocean optics with a QP600-2-VIS-NIR optical fiber (600 core diameter, 24 cm bend and 12 cm radius) over 3 homogenous and dense stands of A.
Mapping of halophilous scrub species distribution The presence of halophilous scrubs was mapped using a Worldview (WV2) very-high resolution imagery (Fig. 1 ). This sensor offered 0. 46 m resolution panchromatic data (450-800 nm) and 1. 85 m in multispectral, delivered at 0. 5 m and 2 m, in 8 bands: B1(400-450 nm), B2 (450-510 nm), B3 (510-580 nm), B4 (585-625 nm), B5 (630-690 nm), B6 (705-745 nm), B7 (770-895 nm) and B8 (860-1040 nm; see sensors details in Table  B. 1). One image was acquired the 31st of August 2016, strictly cloudless, at the time of the year when the global vegetation coverage was estimated to be at its maximum growth. The image covers the entire study area and was delivered in Ortho Ready (Final) level of correction. Multispectral indices (Table  B. 2), among the most used, were adapted to the bands of WV2 following the method of  Davranche et al.
(2010 Davranche et al. ( , 2013) )  and integrated in the database. The Random Forest model (RF) package into the R software (Breiman, 2001) was used to perform image classification. The RF classification algorithm does not work well for unbalanced samples  (Freeman et al. , 2012). The class "presence of halophilous scrubs" represented % of the total observations. Hence, we used the function ROSE Fig. 2. Location and delimitation of the study area, the former saltworks of Salin de Giraud in Camargue (B), south of France (A). Labels refer to hydrological units defined at the time of salt production. (Lunardon et al. , 2014)  from the R package ROSE (with hmult. majo = 0. 20 and hmult. mino = 0. 5) to balance our sample. We first classified both species together, A. macrostachyum; and S. fruticosa, then each species individually.
Sediment data Sediment samples were collected in the summer of 2017. To track the sediment deposition and distribution changes since 2010, we collected the upper 2 cm sediment layer following fieldwork tracks defined in a GIS point layer designed with the QGIS software. The points were placed in a regular grid within the extent of the study area that was specified by a 500-m spacing between the points. The sampling points were then GPS (Garmin Etrex) referenced. Sampling points located in lagoons were discarded, but additional points were sampled in narrow water channels and in the most accessible zones. A total of 396 points were sampled (Fig. 3 ). Grain size analysis of the collected sediment samples was conducted using a Malvern Mastersizer 2000© laser beam grain sizer  (Parsons, 1998; Yu et al.
, 2009). Sand, silt and clay particle content, with their sub-groups (from very coarse to very fine fractions), and the mud estimation (sum of the silt and clay fractions) were extracted according to the Blott & Pye classification using the Excel Gradistat package  (Blott and Pye, 2001). Each sample was characterized by its mean, sorting, skewness and kurtosis indexes (from the Folk and Ward method  (Folk and Ward, 1957) ), with determination of their statistical mode and quantiles (median, quartiles and deciles), to evaluate its textural group  (Pouzet and Maanan, 2020). Variable names are presented in Table  C. 1. The median grain size (d50 in μm) at each sample location was then interpolated over the entire study area. Interpolation was made with the SAGA-GIS  (Conrad et al. , 2015)  software (version 7.
9. 0). Several interpolation methods were tested. To select the best method, we calculated the 10-fold cross validation error for each method. The best result was then selected according to its minimum cross validation errors  (Liang et al. , 2020). The cell size of the resultant interpolation raster was set to 10 m to correspond to Sentinel 2 data resolution. The resultant interpolation map was then categorized following the nomenclature of  Blott and Pye (2001).
Extraction of matches between sediment samples, flooding duration, elevation and halophilous scrubs For each sediment sampling point, we used the "point sampling tool" plugin of the QGIS software to extract the corresponding flooding duration and elevation information. Then, we defined a buffer area of 5 m distance from the points locating the sediment samples and extracted all pixels from the RF map coding for halophilous scrub presence in this zone. Halophilous scrubs were considered present at this location when there was at least one pixel coding for presence in the 5 m radius zone around each point. This approach was thought to optimize the detection of halophilous scrubs at the sampling locations of sediment records, since the point sampling tool may miss the emergence of new vegetation stands and/or sparse developments.
Sediment grain size parameters and halophilous scrub distribution The Boruta package  (Kursa et al. , 2010)  was employed to select the important predictive variables with respect to our dependent variables: flooding duration and vegetation presence. We used the TentativeR-oughFix function of Boruta to get the most important shadow features  (Kursa et al. , 2010). Only variables with a Pearson correlation value below 0. 6 were included in the same model to prevent multicollinearity  (Menard, 2002). The selected variables were then used for regression model building after scaling. Since flooding duration contained 18. 6 % of zero values and considering under and over dispersion, we used negative binomial models and Zero-Inflated Negative Binomial GLM. Presence and absence of halophilous scrubs were modelled using the binomial GLM with a logit link after a rebalancing process with the function ROSE (with hmult.
majo = 0. 20 and hmult. mino = 0. 50) of the R package ROSE  (Lunardon et al. , 2014)  since the category "presence of halophilous scrubs" was representing 12 % of the total observations. Our objective was to find the best predictive model. Hence, the dredge function of the Mumin R package was used to identify the top model (within a range of delta AIC ≤ 2). Resultant model selection was conducted using minimum AIC value and evaluation of residuals with the DHARMa package.
Halophilous scrub distribution mapping The reflectance measured with a field spectrometer showed that the A. macrostachyum spectral curve in the yellow band of WV slightly overlaps with the green band. Compared to S. fruticosa, the reflectance value in the green light is slightly over the yellow and red, most likely due to the physiognomic difference when A. macrostachyum segments turn yellow-brown, S. fruticosa remains green  (Davy et al. , 2006). The difference in spectral responses was confirmed with the classification accuracies from WV2 data (improved accuracies for classification when   the species are considered separately). As stated by several previous studies  (Ali et al. , 2016; Rapinel et al. , 2019; Vanden Borre et al. , 2011) , this result confirms that field experts and conservation site managers must work together on data standardization and knowledge sharing for improving the use of remote sensing to fulfil Natura 2000 habitat monitoring.
The validation on an independent sample provided a good overall accuracy for both species. This validation shows that other land cover types, habitats and species are well identified as "absence of halophilous scrubs". However, results showed an underestimation of the presence of both species, especially S. fruticosa (omission error 44 %). The validation sample was made with data acquired from 2010 to 2013 according to the identification of Natura 2000 habitats with no assurance that the stands of A. macrostachyum and S. fruticosa were in the same ecological state in 2016. Also, cover percentages were not specified in the available database and sampling points placed in the polygons could fall on very sparsely vegetated areas. Some of them might have been placed on bare soil or on a mix of halophilous scrubs with other species that might partly explain the error of omissions with this validation sample.
Our study area is very flat, but the LIDAR data provided bathymetry up to -13 m under the water surface close to the border of the beach in the sea. This approach offers great potential to apply the approach in coastal regions where the topography and depths are important drivers. According to our results, in very, flat micro-intertidal areas, like the Salins de Giraud, multispectral indices, such as the WIW, seems to be helpful in understanding the water pathways and their impact on vegetation development related to sediment grain size. Hence, the high acquisition periodicity of SENTINEL images offers a useful tool to understand the impact of extreme storm water inflow in the process of sea-level rise, since it can highlight and assist in measuring the surfaces that are rarely inundated. In this study, we considered flooding duration using one or two images monthly but the constellation of SENTINEL 2 with a third satellite to be launched in 2024, will increase the temporal resolution.
Acknowledgments Funding was provided by the  Agence de l'eau Rhône Méditerranée Corse  and the  MAVA Foundation. Aurélie Davranche  was supported by the  CNRS  and the  University of Angers  for three 6-months research leaves. The  Teledyne Optech  Titan DW LIDAR sensor used in this study is operated by the  Nantes-Rennes Topo-Bathymetric lidar platform (University of Rennes 1/University of Nantes)  and has been funded by the  Region Pays de la Loire  with funding of the  RS2E-OSUNA programs  and the  Region Bretagne  with support from the  European Regional Development Fund. The Camargue reserve provided access for field sampling. A. Rita Carrasco  was supported by the contract  DL57/2016/CP1361/ CT0002. A. Rita Carrasco  and  Alice Newton  acknowledge  LA/P/0069/ 2020  and  UID/00350/2020 CIMA , all funded by  Fundação para a Ciência e Tecnologia.
Data availability The Sentinel 2 images used to calculate the flooding duration were value-added data processed by the CNES for the Theia www. theia. land. fr data cluster using Copernicus data. The treatments use algorithms developed by Theia's Scientific Expertise Centres. The LIDAR DTM and the sediment sampling database are available from the corresponding author upon request. The rasters of halophilous scrub distribution in 2016 and the interpolation of median sediment grain size were shared online (see DOI provided in the reference list).
Estimation of Meteorological condition at the summit of the Holtedahlfonna ice field In absence of in-situ meteorological measurements at the drill site, we obtained long-term seasonal (DJF, MAM, JJA and SON) temperature and precipitation series from the high-resolution CARRA dataset (Copernicus Arctic Regional Re-Analysis,  Schyberg et al. , 2020). This 2. 5 km resolution product covering the period 1991-2020 is downscaled from ERA5  (Hersbach et al. , 2020)  using the state-of-the-art weather prediction model HARMONIE-AROME  (Bengtsson et al. , 2017). CARRA has several improvements compared to ERA5, including assimilation of a large amount of additional surface observations, extensive use of satellite data, and improved representation of sea ice; it is therefore likely to provide the best estimate of meteorological conditions in the Barents Sea region.
Acknowledgments This work has been supported by the " Programma di Ricerca in Artico"  ( PRA , project number  PRA2019-0011 , Sentinel); by the  Svalbard Science Forum/Research Council of Norway  through the  Arctic Field Grant  call (project  ASIHAD ,  ISSICOS ,  BIOMASS ), by  French Polar Institute IPEV (Institut Polaire Français Paul-Emile Victor)  science funding (programs  399  and 1192) and the  Svalbard Strategic Grant  (project  C2S3 , nr. 257636 , SnowNet nr. 295779  and BC3D nr. 283466 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 689443  via project  iCUPE (Integrative and Comprehensive Understanding on Polar Environments). This research has been partially funded by the  University of Perugia Research Action  no.
2006; Estournel et al. 2023 ). The study site (station Z, water depth 20 m, Fig. 1 ) is located on the delta front, at a distance of 2 km from the river mouth, and is characterized by a mean apparent accumulation rate of up to 35 to 48 𝑐𝑚 𝑦𝑟 -1  (Charmasson et al. 1998). The site is defined by geographic coordinates (lat. 43°19. 1°N and long. 04°52. 0°E), but the constraints of sea work (e. g. ship 105 drift) lead to a positioning variability estimated at a perimeter of 60m around these coordinates. 1 ) when the weather permitted with sampling cruises onboard the RV Antédon II (IFREMER-FoF). The Rhône River flows were recovered from the Hydroréel database at the Tarascon-Beaucaire station (hydrometric station V720001002). The SPM content are recovered from the database of the Rhône Sediments Observatory.
Stoichiometric ratio The anaerobic mineralization of buried organic matter by sulfate reduction (Reaction R1) and the anaerobic methane oxidation (AOM, Reaction R2) reactions provide theoretical, stoichiometric ratios (𝑟 𝑐:𝑠 ) of 𝑆𝑂 4 2-consumption to bicarbonate ion production. 155 2𝐶𝐻 2 𝑂 + 𝑆𝑂 4 2-→ 2𝐻𝐶𝑂 3 -+ 𝐻 2 𝑆 (R1) 𝐶𝐻 4 + 𝑆𝑂 4 2-→ 𝐻𝐶𝑂 3 -+ 𝐻𝑆 -+ 𝐻 2 𝑂 (R2) This ratio can be used to identify the key process that dominates mineralization in sediments from porewater measurements  (Burdige and Komada 2011). The 𝑟 𝑐:𝑠 (Equation  1 ) of the present dataset were calculated as described by  Burdige and Komada (2011). Before the flood event 𝑟 𝑐:𝑠 were calculated on the whole core. After the deposition event, two 𝑟 𝑐:𝑠 were calculated as a function of depth, the first on a surface layer between 1 and 25 cm and the second from 25 cm to the bottom of the core.
Sulfate-Methane dynamics before and after the flood 390 In anoxic sediments, the carbon cycle is tightly coupled to sulfur/methane cycles  (Jørgensen and Kasten 2006). The present dataset and model can be used to understand the impact of flood deposition on these coupled cycles. In the case of the sulfur cycle, 90 % of oceanic sulfate reduction takes place in sediments of the continental shelves  (Jørgensen 1982; Jørgensen et al. 2019). The two main pathways for sulfate reduction is organoclastic sulfate reduction (OSR) that depends on the lability and amount of degradable organic matter and anaerobic oxidation of methane (AOM) where methane is anaerobically oxidized 395 to bicarbonate using 𝑆𝑂 4 2-as electron acceptor by a consortium of microbes including bacteria and archaea  (Jørgensen et al.