dataesr/scientific-paragraphs-categorization huggingface dataset:
Several precautions were taken to account for the low coverage and the existence of single DNA reads: the ends of the reads were trimmed to eliminate potential post-mortem damage and C-T and G-A substitutions were only considered when they were shared with other rodent species. We further inferred a time-calibrated Bayesian phylogenetic tree, relying on three secondary calibration points based on the results of Aghová et al. [13]. Bayesian inference (BI) was used to estimate the phylogenetic relationships and node ages using the BEAST v. 2. 6. 5 package  [14]. The multiple sequence alignment was built using MAFFT software  [15]. We first aligned arvicanthin sequences and then we added to this alignment the sequence of †C. bravoi using the -add option in MAFFT. Best fit model of nucleotide substitution for this alignment was elected with jModelTest  [16]  based on the Bayesian information criterion.
The Indian and East Asian Summer Monsoons shape the melt and accumulation patterns of glaciers in High Mountain Asia in complex ways due to the interaction of persistent cloud cover, large temperature ranges, high atmospheric water content and high precipitation rates. Glacier energy and mass balance modelling using in-situ measurements offer insights into the ways in which surface processes are shaped by climatic regimes. In this study, we use a full energy-and mass-balance model and seven on-glacier automatic weather station datasets from different parts of the Central and Eastern Himalaya to investigate how monsoon conditions influence the glacier surface energy and mass balance. In particular, we look at how debris-covered and debris-free glaciers respond differently to monsoonal conditions.
The local climate influenced by the East Asia Monsoon with typically only 50 to 60% of the annual precipitation arriving during the monsoon period (Figure  1  and S1). The debris-covered tongue is connected to a steep and extensive accumulation zone via a large icefall, but avalanching is the primary mass supply mechanism through the icefall to the valley tongue  (Liao et al. , 2020) , and a dynamic disconnect is expected to occur in the near future. Weather stations were installed at three nearby off-glacier locations and one on-glacier site during 2008, while precipitation was measured at the Alpine Ecosystem Observation and Experiment Station of Mt. Gongga, within 1. 5 km from the glacier terminus (Table  1 ). We use the monthly averaged ERA5-Land reanalysis data  (Muñoz Sabater, 2019)  to evaluate the representativeness of the AWS records in terms of seasonal variability  (Figures S2 to S8) , and to provide an overview of the long term climatic patterns, e.
Precipitation partition Precipitation is partitioned into solid P r sno and liquid P r liq precipitation, because of the differing impacts of snow and rain on the energy and mass balance. For this study, the precipitation partition method described by  Ding et al. (2014)  was implemented into the T&C model. This parameterisation has been developed specifically for High Mountain Asia based on a large dataset of rain, sleet and snow observations, and does not require recalibration. It determines the precipitation partition based on the wet-bulb temperature, station elevation and relative humidity and allows for sleet events, as a mixture between liquid and solid precipitation. Ding et al. (2014)  found the wet-bulb (T wb ) to be a better predictor than T a of the precipitation type. They also found that the temperature threshold between snow and rain increases at higher elevations, and that the probability of sleet is reduced in conditions of low relative humidity.
Second, the timing and quantity of post-monsoon and winter precipitation influence the annual mass balance, however, even fewer datasets exist for the winter half-year in HMA, preventing a yearround analysis of similar detail. Third, all our sites are located in glacier ablation areas, and surface and energy mass fluxes will change with elevation. While we have tested how representative our point-scale results are for the entire ablation area of the glaciers considered, the response of glacier accumulation areas to monsoon remains to be investigated. Meteorological data from accumulation areas are scarce, however, limiting our current understanding. Future work should establish new year-round and multi-year records, including datasets from accumulation areas, in order to extend some of our findings.
Acknowledgements. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  772751 ), RAVEN, " Rapid mass losses of debris-covered glaciers in High Mountain Asia ". We also thank the  National Natural Science Foundation of China  ( 41961134035 ) for financially supporting the data collection on 24K and Parlung No. 4 glaciers. We would like to also thank  Jakob Steiner  and  ICIMOD  for hosting and contributing datasets. Our special thanks go to  Marin Kneib  for organizing the field campaigns, and the  Langtang  and  24K  field teams for helping with data collection.
Environmental parameters We characterized the temperature regime that induced bleaching at our sites using different techniques. First, we computed the cumulative heat stress as degree heating weeks (DHW) from NOAA Satellite Sea Surface Temperatures  [25, 26]. Second, because satellite measurements are relevant only for shallow waters, we also deployed in situ temperature (HOBO Water Temperature Pro v. 2 Data) and light (DEFI2-L JFE Advantech) loggers that measured light for photosynthetically active radiation (PAR) at the different sampling depths. A detailed explanation of how these data were collected, normalized according to 6 m depth because it was the departing reference for loggers and bleaching assessments and processed using the Beer-Lambert equation  [27, 28]  are available in electronic supplementary material, figures S2 and S3.
We used uninformative priors and ran the model for four MCMC chains using 7000 iterations per chain after 3000 iterations of warm-up. We used the posterior likelihood of being healthy at the shallowest occurrence of each genus as a benchmark to account for genus-specific bleaching resistance. Similarly, for each genus, we calculated the posterior likelihood of being healthy at their deepest occurrence to evaluate depth-driven health benefits. All analyses were performed in RStudio. Data and codes are available in https://github. com/gonzaloprb/Deep_Bleaching_French_Polynesia.
Acknowledgements. We sincerely thank the team of the Under The Pole Expedition III. Under The Pole Consortium:  G. Bardout ,  J. Fauchet ,  A. Ferucci ,  F. Gazzola ,  G. Lagarrigue ,  J. Leblond ,  E. Marivint ,  A. Mittau ,  N. Mollon ,  N. Paulme ,  E. Périé-Bardout ,  R. Pete ,  S. Pujolle ,  G. Siu.
Acknowledgments It is a pleasure to thank  Joel Bergé  and  Jeremy Sakstein  for helpful discussions. This work was partially supported by  STFC  Consolidated Grants No. ST/P000673/1 , No. ST/P000681/1 , and No. ST/T000694/1. S. M. is supported by an  Emmanuel College Research Fellowship. This work was made possible by the  Institut Pascal at Université Paris-Saclay  with the support of the  P2I  and  SPU research departments  and the  P2IO Laboratory of Excellence (program  " Investissements d'avenir "  ANR-11-IDEX-0003-01   Paris-Saclay  and  ANR-10-LABX-0038 ), as well as by the IPhT.
An Overview of the Case Study, Measurements and New Parameterization of Nucleation Etna volcano (3330 m above the sea level, located at 37. 748 • N, 14. 99 • E; Italy) is the largest regional source of gas and particles that are transported in the FT by winds for long distances covering a significant portion of the Mediterranean Sea  [67]. A study performed in the context of the Aerosol Comparisons between Observations and Models (AEROCOM) program  [68] , reported that when considering the daily SO 2 emissions and plume heights above the vent for 1167 volcanoes from 1 January 1979 to 31 December 2009, Etna was the largest contributor, with more than 45 Tg. This was also evidenced by the NASA Modeling, Analysis, and Prediction (MAP) program  [69, 70]. The particle formation rate as a function of sulfuric acid concentration under volcanic plume conditions was examined by  [37]  from the aircraft measurements performed during the STRAP (Synergie Transdisciplinaire pour Répondre aux Aléas liés aux Panaches Volcaniques) project in the period of 15-16 June 2016 over Etna and Stromboli volcanoes.
Domain Characteristics The WRF-Chem, model version 3. 9, was used for high-resolution numerical simulations over three nested domains (with increasing spatial resolution: 9, 3, and 1 km resolution for d01, d02, and d03, respectively; Figure  1 ). The topography data for the innermost domain were extracted from a high-resolution SRTM dataset (the NASA Shuttle Radar Topographic Mission 90 m Digital Elevation Data) at 3 s (about 100 m) horizontal resolution (https://srtm. csi. cgiar. org/, accessed on 15 December 2020) and the land use data used a CORINE (coordination of the information on the environment) Land Cover inventory with 33 different land use/land cover classes. For the two outermost domains (d01 and d02), the USGS (United States Geological Survey) land cover dataset was used. Meteorological initial and boundary conditions were taken from the ERA5 reanalysis data  [74]  with a temporal resolution of 6 h.
Emissions WRF-Chem has proven to be an excellent candidate for the prediction of the formation, transport, dispersion, and sedimentation of different types of natural emissions  [85]. A small number of recent studies focused on the effects of volcanic emissions within the atmosphere for eruptions or passive degassing at various volcanoes: Kasatochi volcano, 2008  [86] , Eyjafjallajökull volcano, April-May 2010  [87] , Etna volcano, 2015 [8], Grimsvötn volcano, 2011  [88] , Eyjafjallajökull volcano, 2010  [89] , and Mount Redoubt volcanic ash clouds using coupled PUFF and WRF-Chem dispersion models, 2009  [90] ), as well as gasphase species, aerosol, and cloud properties  [9, 91]. In some of these studies, a module  [85]  was implemented to WRF-Chem to generate volcanic ash and sulfur dioxide emitted during the volcanic eruptions and passive degassing from a global emission database  [92] , based on field regional observations.
The work described hereafter is focused on the passive degassing of the Etna volcano observed on 15 June 2016 (as described in Section 2. 1; Table  1 ) where only the products of passive emissions (no ash) could be recorded  [37]. To accurately represent the level of sulfur dioxide emission, we did not use the global emission database  [92]. Nevertheless, in a similar way, a continuous source of SO 2 mass flux was introduced to the model at the nearest grid point to the real Etna coordinates (located at 37. 748 • N, 14. 99 • E; 3330 m above the sea level) at every meteorological time step. However, due to the spatial resolution of the model and the mismatch between the topography heights considered in the model and the real values, the location of the simulated volcanic plume showed horizontal and vertical shifts (see Section 2.
Acknowledgments: The lead author and this work were funded by ClerVolc Project-Programme 1, "Detection and characterization of volcanic plumes and ash clouds", funded by the French government's "Laboratory of Excellence" initiative no. ANR-10-LABX-0006 and the Region Auvergne. This is Laboratory of Excellence ClerVolc contribution number no. 515. The aircraft measurements were carried out within the CLerVolc and STRAP (Synergie Transdisciplinaire pour Répondre aux Aléas liés aux Panaches volcaniques) projects thanks to the SAFIRE facilities in the period of 15 and 16 June 2016 over Etna and Stromboli volcanoes, and this database is available on the STRAP website (http://osur. univ-reunion. fr/recherche/strap/database/, accessed on 13 January 2021). We thank all the people who contributed to the measurements and/or pre-processing of the data and M.
Barrier islands are indicators of coastal resilience. Previous studies have proven that barrier islands are surprisingly resilient to extreme storm events. At present, little is known about barrier systems' resilience to seismic events triggering tsunamis, co-seismic subsidence, and liquefaction. The objective of this study is, therefore, to investigate the morphological resilience of the barrier islands in responding to those secondary effects of seismic activity of the Sumatra-Andaman subduction zone and the Great Sumatran Fault system. Spatial analysis in Geographical Information Systems (GIS) was utilized to detect shoreline changes from the multi-source datasets of centennial time scale, including old topographic maps and satellite images from 1898 until 2017. Additionally, the earthquake and tsunami records and established conceptual models of storm effects to barrier systems, are corroborated to support possible forcing factors analysis.
Studies about the influence of the secondary effects of tectonic activities on the barrier island and spit morphological development are currently relatively limited. This study aims to investigate the morphological resilience of the barrier islands in responding to tsunamis, co-seismic subsidence, and liquefaction. Spatial analysis was utilized to detect shoreline changes from the multi-source datasets of the multi-decadal time scale, including old topographic maps and satellite images from 1898 until 2017. As the next step, we identified the history of major or moderate earthquakes that occurred in each consecutive period. Subsequently, we corroborated those earthquake events with the reported impact of the earthquakes on the coastal areas. We also utilized the established conceptual models of storm effects to barrier systems to interpret possible controlling factors causing the remarkable morphological changes observed at two selected coastal sections.
Here, we limit the area of earthquake influence by setting up a regional boundary within which we identify any major (M ≥ 7. 0) and moderate (6. 5 ≤ M < 7. 0) earthquake events records, as depicted in Figure  5. The historical earthquake records are obtained primarily from the NGDC/WDS Global Historical Tsunami Database of the National Centers for Environmental Information (NCEI) database  [54]. Additionally, records were also obtained from the Catalogue of Significant and Destructive Earthquakes 1821-2017 for the earthquakes associated with the Great Sumatran Fault  [55]. From the database we found five remarkable earthquake events which occurred within our investigation period, which are enlisted in Table  2. The locations of the earthquake epicenters are depicted in Figure  5. Regional earthquake occurrences at the northern Sumatra during the investigation period in the present study (1898-2017).
The dashed-line rectangle shows the regional boundary of influence of the major (M ≥ 7. 0) and moderate (6. 5 ≤ M < 7. 0) earthquake records. Records of earthquakes M ≥ 7. 0 accompanied with the validity of tsunami occurrences are retrieved from the earthquake catalog by  [54] , and the tectonic earthquakes associated with the Great Sumatran Fault obtained from  [55]. Note: * Data source from  [54] ; 4 = definite tsunami; 3 = probable tsunami; 2 = questionable tsunami; 1 = very doubtful tsunami; 0 = event like a seiche. ** Data source from  [55]. The NCEI database is accompanied by the reports of the past earthquakes and their associated resulting hazards (e. g. , tsunamis, earthquake damage, liquefaction, etc. ) that were mostly archived by Soloviev and Go  [56]  and also by several other scientific documents and papers.
Regular Wave Climate and Littoral Transport Rate We obtained the wave climate of the Banda Aceh coast by converting the eleven-year daily wind data records from 1995 to 2005 from the National Meteorology, Climatology and Geophysics Agency (BMKG), and subsequently translated into statistical wave heights and periods as functions of wind velocity, duration, and fetch. Surface and tidal currents are not well-recorded in this coastal region. The tides moderately range of 1. 00 m from MHWL to MLWL. From the data, we found that the prevailing monsoonal wave directions were from the northwest and the northeast. Based on the resulting wave data, we then roughly estimate the littoral transport rate by using the simple CERC formula  [62]  at both Lambadeuk and Kuala Gigieng coasts. Estimates of net littoral transport along both coasts will be discussed in Section 4.
Rate of Change of Barrier Islands Morphology From the wave data analysis, we obtained the seasonal prevailing monsoonal waves annually. The southwest monsoon occurs between April and September and is characterized by relatively rough waves coming from the northwest at the Banda Aceh coast. About 53% of the waves approach the coast with a significant wave height of 1. 0 m with a period of 3 s. During the northeast monsoon between October and March, the climate tends to be milder, with 30% of the waves approaching from the northeast with a significant wave height of less than 1 m and a period of 4. 5 s. Based on the resulting wave data, we then roughly estimate the littoral transport rate by using the simple CERC (CERC stands for Coastal Engineering Research Center, U. S. Army Engineer Waterways Experiment Station, Vicksburg, MS) formula  [62] , which results in the estimate of net littoral transports at a rate of +0.
We propose a non-exhaustive list of forcing parameters that can control the initial rifting conditions but also their evolution through time. Therefore, rifted margins are not divided into opposing types, but described as a combination and continuum that can evolve through time and space. Observations along rifted margins are now more accurate thanks to the increasing number of deep high-quality 2D/3D seismic datasets acquired. They provide images of the deep levels of the crust, its internal geometries, structures and seismic facies, especially within the Continent-Ocean Transition (COT). Coupled with wide-angle seismic and gravity/magnetic data, which give an idea of the velocity/density gradient and layering of the crust, they allow a much better resolution of the large-scale character of a margin.
described (weak and strong mechanical behaviors) but, thanks to thermomechanical modelling, a wide spectrum of intermediate behaviors was explored. Based on the interpretation of deep seismic datasets across many margins and the existing knowledge, we present a classification based on two axes (Fig. 1 ): Mechanical Behavior, reflecting the deformation style of the crust (weak to strong mechanical behavior) and magmatic production, reflecting the quantity of magma involved during rifting and breakup (magma-poor to magma-rich). The main seismic characters, geometries and large-scale correlations are illustrated through four contrasting cases located along the Somalia, Uruguay, Gabon and Coral Sea margins (Fig. 2 ). After describing their structural variability, we discuss this classification and the lithospheric processes involved.
On other profiles from our dataset, the upper continental basement of the margin shows indications of a pre-existing fold-and-thrust belt, with some thrusts inverted as low-angle detachments. Another striking feature is the scarcity of syn-rift deposits with some of them being even eroded before the deposition of post-tectonic sediments. Such patterns can be explained through a core-complex model in a thermally-supported setting (e. g. Basin-and-Range province  70  ) involving progressive uplift of the detachment fault and of its rider blocks through a rolling hinge mechanism  [71] [72] [73]. Several of these core-complexes and short normal faults rooting at the interface between the upper and middle crustal layers are visible along the profile. However, large scale correlations attribute these structures to the previous orogenic collapse  74.
Acknowledgements This paper is the result of 10 years of structural studies on new deep seismic datasets in Total with an input of  J. -F. Ballard ,  T. Maurin ,  M. Masini ,  M. Denis. It also integrates the knowledge acquired through R&D in various projects (PN/GRI, EBS, RTPM) and fruitful interactions with G. Manatschal,  L. Geoffroy ,  L. Jolivet ,  G. Mohn ,  M. Nirrengarten ,  F. Chauvet  and  J. Tugend. The authors are in debt to all of them. The authors also thank  CGG, ION, Searcher  and  TGS  who kindly authorized us to publish their high-quality seismic data. We thank  C. Ribes  and  C. Wibberley  for proof-reading. Our reviewer,  Dr. Gwenn Peron-Pinvidic ,  Dr. Dieter Franke  and  Pr. Ritske Huismans  helped a lot to improve the quality of this manuscript.
First reported in the 1960s, offshore freshened groundwater (OFG) has now been documented in most continental margins around the world. In this review we compile a database documenting OFG occurrences and analyze it to establish the general characteristics and controlling factors. We also assess methods used to map and characterize OFG, identify major knowledge gaps, and propose strategies to address them. OFG has a global volume of 1 × 10 6 km 3 ; it predominantly occurs within 55 km of the coast and down to a water depth of 100 m. OFG is mainly hosted within siliciclastic aquifers on passive margins and recharged by meteoric water during Pleistocene sea level lowstands. Key factors influencing OFG distribution are topography-driven flow, salinization via haline convection, permeability contrasts, and the continuity/connectivity of permeable and confining strata.
Here we update the global database of OFG records in  Post, Groen, et al. (2013)  and analyze this in the context of recent and past studies to (i) establish the current understanding of the general characteristics, emplacement mechanisms, and controlling factors of OFG in continental margins; (ii) present the geophysical, geochemical, and modeling approaches that are currently used to detect and characterize OFG; (iii) identify the major knowledge gaps and uncertainties that exist with respect to the above and make recommendations on how these should be addressed; and (iv) explore two frontier applications of OFG research (exploitation as a water resource and geomicrobiology) that will benefit from an improved understanding of OFG. Passive and convergent margins  Moosdorf and Oehler (2017; Taniguchi et al.
Characteristics and Controls of OFG in Continental Margins We establish the general characteristics, emplacement mechanisms, and controlling factors of OFG systems by compiling a database of 305 records of groundwater located in continental margins  (Micallef, 2020). The criteria for including records in this database include salinities of ≤33 PSU, a location of ≥1 km away from the coast, and a distance of >10 km from other records (Figure  1 ). The majority of the information (84. 6%) is sourced from borehole and core data. The rest is derived from marine electromagnetic (EM) surveys, observations of submarine groundwater discharge (SGD), and potential onshore indicators (e. g. , fresh coastal groundwater dating to last glacial period; semi-confined coastal aquifers where  [i]  the onshore piezometric head is larger than the equivalent freshwater head or  [ii]  where the onshore freshwater is close to the shoreline despite the piezometric head being smaller than the equivalent freshwater head;  Knight et al.