dataesr/scientific-paragraphs-categorization huggingface dataset:
It is valuable to include ideas that are shaped through intergenerational cooperation; it improves internal and potentially boosts external communication. As a result, within AtlantOS there is (1) a representative ECS in the steering committee for the project and (2) a representative ECS on the gender diversity board. Such linkages should be promoted and encouraged. These ECSs will also bring a fresh perspective on existing ethical issues and provide guidance to all on new and unanticipated ethical issues that may arise in the future. Civil society engagement has the potential to inform and sensitize society through education and direct participation in an action. Contributions from citizens can range from collecting data and information to data analysis and can be based on incidental observations as well as on standardized surveys and monitoring protocols.
, 2015). These animals sample ocean areas that are inaccessible or unaffordable to sample with our current technologies (e. g. under ice sheets in polar areas) and provide data that can be of great benefit for humans (e. g. enabling more accurate climate and weather forecasting and a better understanding of ocean circulation;  Roquet et al. , 2014). However, the use of animals for research has generated a widespread, passionate and evolving debate surrounding animal sentience, their rights and the responsibilities of humans towards animals (e. g. Rollins, 2002). There is broad acceptance internationally that animal research needs to be conducted humanely. The Ocean Tracking Network (OTN) is a major global animal research network charged with using electronic telemetry to document the movement and survival of marine and freshwater animals.
Most telemetry organizations have adopted policies that implement a time delay (1-2 years) before data can be made public (unless the originators of the data agree to earlier public release). This is to, among other concerns, protect the rights of students to finish their research projects. In addition, there are ethical issues associated with the potential for misuse of the public data, such as the provision of the real-time locations of highly valued and possibly endangered species to poachers (see  Cooke et al. , 2017). The main recommendations for respect for animals in ocean observing systems are as follows. -Ensure the animal's good health: identify the levels of distress that could be caused to animals by their capture, handling and labelling, and provide means to mitigate these problems; use appropriate anaesthesia and aseptic surgery procedures, oxygenate-air the animals, ensure that surgeons are trained and perform their tasks quickly, use appropriate recovery facilities, and return the animals to the wild at a time and place that minimize the risk of predation.
" In ocean observation, data are elements coming from sensors and observation, and they are relative to the measured level of any variables. Information consists of data organized in a given structure and which, placed in a context, are endowed with meaning. Knowledge goes further: it makes predictions to establish causal links or make decisions  (Bohn, 1994). Earth stewardship requires that scientists engage not only in the production of this knowledge, but also in meeting societal requirements for education and governance  (Rozzi et al. , 2015). Interaction with policymakers is evolving and will become commonplace as the Anthropocene era is based on scientific evidence. Science and policy interface is essential at the national level but also at the regional and global level  (Bernal and Simcock, 2016).
The policy brief on the Gulf Stream will be discussed during a science policy event at the European Parliament. AtlantOS has been also actively involved in strategic and political debates and social science activities such as the UN Ocean Conference on Sustainable Development Goal 14, the UN COP 23 meeting, the Vision 2030 for ocean observing in Europe meeting, and the initiation of the BluePrint group for ocean observation implementation and vision. Ethical behaviour in science should be encouraged and its teaching should become a common requirement at all stages of education process. Ethical behaviour is essential and even natural for scientists in carefully measuring, using appropriate standards, repeating the measurement, assessing uncertainty, and reporting data and uncertainty so that others can examine them  (Bobrowsky et al.
, 2016). Training is strongly linked to capacity building. Capacity building is essential for developing observations in areas where they do not exist and could facilitate the interoperability of data collected by different people through different means  (Muller-Karger et al. , 2018). In capacity building, the place of young scientists is important, and some programmes are implemented to train and offer access to cruises for earlystage researchers. The POGO and PIRATA programmes offer seats for joint cruises and host exchanges to reinforce collaboration and learning established and new methodologies. These missions offer a unique opportunity to expose students to an intensive working atmosphere, to forge professional relationships between students, junior and senior scientists, and to understand different cultures and international differences in knowledge generation, communication and use.
Acknowledgements. The authors thank  Martin Visbeck  for encouraging the publication of this paper,  Bernard Bourles  for his comments and the reviewers for their remarks in helping to improve the quality of the paper. Katsiaryna Pabortsava ,  Anne-Cathrin Wölfl  and  Tobias Hahn  have received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 633211  (AtlantOS).
Acknowledgments: We thank  V. Ryazanov ,  V. Dremov , and  V. Vinokur  for fruitful discussions and advice. A. I. B. wish to thank the  Leverhulme Trust  for supporting his stay in Cambridge University. Funding: This work was supported by the  French National Agency for Research  (grants MISTRAL, SUPERSTRIPES, and SUPERTRONICS) and the  Ministry of Education and Science of the Russian Federation  (grant  14. Y26. 31. 0007 ). V. S. S. acknowledges the partial financial support of the  Ministry of Education and Science of the Russian Federation in the framework of Increase Competitiveness Program of National University of Science and Technology MISiS  (no. K3-2017-042 ). V. S. S acknowledges the partial support by the  Program of Competitive Growth of Kazan Federal University. D. R. , V. S. S.
, and S. Y. G. acknowledge the partial financial support within the framework of the state competitiveness enhancement program of improving the prestige of leading Russian universities among world leading research and education centers. The sample fabrication and magnetometry characterization were supported by the  National Science Foundation of China (NSFC)  (grants  11474252 ,  11611140101 , and  U1432135 ). We also thank the  Russian Foundation for Basic Research  ( 16-02-00727  and  17-52-50080 ). This work was also supported by a  Grant-in-Aid for Scientific Research (A)  ( 17H01141 ) and by the  Japan Society for the Promotion of Science/NSFC  under the  Japan-China Scientific Cooperation Program. Part of this work was performed using equipment of  Moscow Institute of Physics and Technology Shared Facilities Center  and with financial support from the  Ministry of Education and Science of the Russian Federation  (grant no.
Acknowledgements The authors would like to express their heartfelt gratitude to  Dr. Rukhsana Perveen , who provided guidance, necessary facilities, and took a keen interest throughout the progress of the present research. We would like to extend our deep gratitude and sincere thanks to  Dr. Claudio Canepari , an authority on the family Coccinellidae, who spent his valuable time assisting us in identification, confirmation, and guidance. We are grateful to  Dr. Louis Hesler ,  Lead Scientist & Research Entomologist ,  USDA-ARS, North Central Agricultural Research Laboratory (NCARL) ,  U. S. A,  for his review and English proofing of this paper.
We calculated AAR 0 values of 0. 65 for the Zongo glacier (r 2 = 0. 73), 0. 72 for the Antizana (r 2 = 0. 83), and 0. 68 for the Artesonraju (r 2 = 0. 77; fig. S12 ). We used an AAR range of 0. 63 to 0. 73 to take into account the uncertainty associated with the paleo-ELA determination. The hypsometry data required for the AAR method were derived from the Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global digital elevation models from NASA-U. S. Geological Survey (USGS). The glaciers used for the calibration have various orientations, and the AAR range that we used only resulted in 30 to 50 m uncertainty on the ELA values, making the spatial ELA gradient highly significant. As AAR values are sensitive to the climate settings, we moreover propose an iterative use of our model that optimizes spatialized values of AARs over the basin in section S4.
Glacier modeling We combined glacier and lake modeling in an inversion algorithm to identify a precipitation grid for the Altiplano basin that satisfied both glaciers and lakes extents during the Lake Tauca highstand. For the glacier model, we used the ELA = f(P,T) model of Condom et al. (33)  ELA ¼ 3427 À 1148 Â log 10 ðPÞ þ TðzÞ=LR þ z ð1Þ where ELA is the elevation of the ELA in meters above sea level, P is the annual rainfall in millimeters, T is the mean annual temperature in degrees Celsius, LR is the atmospheric lapse rate in degrees Celsius per meter, and z is the altitude of the temperature measurement in meters above sea level. Present-day climate variables for the nine glacial sites were derived from meteorological station data  (37). We derived identical results (precipitation map and uniform cooling) when repla-.
Acknowledgments: We thank  P. Burnard ,  L. Zimmerman ,  Flo Gallo ,  M. Protin ,  L. Tissandier ,  Y. Marrocchi ,  Benji Barry ,  C. Hautefeuille , and  A. Williams  for their vital contributions to various aspects of this study. We are grateful to the  Service d'Analyse des Roches et des Minéraux (SARM)-CNRS  for high-quality chemical analysis and to the  STEVAL (Station Expérimentale de VALorisation) crew (GeoRessources, Nancy)  for their technical support with sample crushing and mineral separation. The logistical support of the  IRD of La Paz (Bolivia)  during our field trips conducted between 2010 and 2013 was greatly appreciated. We thank the three anonymous reviewers for constructive and stimulating feedbacks that significantly improved the manuscript. Funding: This work was funded by the  INSU EVE-LEFE  ( Institut National des Sciences de l'Univers Evolution et Variabilité du climat à l'Echelle globale-Les Enveloppes Fluides et l'Environnement) program  and the  Agence Nationale de la Recherche (ANR) Jeunes Chercheurs GALAC (GlAciers et LACs de l' Altiplano)  project (grant no.
ANR-11-JS56-011-01 ). The  ASTER French National AMS Facility (CEREGE)  is supported by the  INSU/CNRS , the  ANR  through the  "Equipements d'Excellence" program ,  IRD , and  Commissariat à l'Energie Atomique et aux Energies Alternatives. The field measurements of mass balance and ELA on the glaciers in Bolivia and Ecuador were provided by the Andean part of the  Service National d'Observation GLACIOCLIM  funded by the  French IRD , the  Universidad Mayor de San Andres [IGEMA (Investigaciones Geológicas y del Medio Ambiente) ,  IHH  ( Instituto de Hidráulica e Hidrología )] in Bolivia and the  Insituto Nacional de Meteorologia e Hidrologia in Ecuador. Support from the  International Joint Laboratory GREAT-ICE  ( Glaciers et Ressources en Eau des Andes Tropicales-Indicateurs Climatiques et Environnementaux ) and the  LabEx OSUG@2020  ( ANR grant  no.
ANR-10-LABX-56 ) are also acknowledged. Author contributions: Field work: P. -H. B. , J. L. , T. C. , V. J. , D. B. , M. L. , J. C. , V. M. , and L. C. P. M. ; CRE dating: L. C. P. M. , P. -H. B. , J. L. , B. T. ,  ASTER Team , and E. D. ; modeling: L. C. P. M. , J. L. , P. -H. B. , M. P. , and T. C. ; writing and discussions: L. C. P. M. , P. -H. B. , J. L. , V. J. , T. C. , M. L. , and  J. C. Competing  interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. Members of the ASTER Team:  Maurice Arnold ,  Georges Aumaître ,  Didier L. Bourlès ,  Karim Keddadouche ,  Aix-Marseille Université ,  CNRS ,  IRD ,  Collège de France ,  CEREGE-UM34 ,  Technopôle de l' Arbois ,  BP80 , 13545 Aix-en-Provence, France.
We employ National Aeronautics and Space Administration (NASA)'s Operation IceBridge high-resolution airborne gravity from 2016, NASA's Ocean Melting Greenland bathymetry from 2015, ice thickness from Operation IceBridge from 2010 to 2015, and BedMachine v3 to analyze 20 major southeast Greenland glaciers. The results reveal glacial fjords several hundreds of meters deeper than previously thought; the full extent of the marine-based portions of the glaciers; deep troughs enabling warm, salty Atlantic Water (AW) to reach the glacier fronts and melt them from below; and few shallow sills that limit the access of AW. The new oceanographic and topographic data help to fully resolve the complex pattern of historical ice front positions from the 1930s to 2017: glaciers exposed to AW and resting on retrograde beds have retreated rapidly, while glaciers perched on shallow sills or standing in colder waters or with major sills in the fjords have remained stable.
Plain Language Summary Over the last century, the glaciers in southeast Greenland have exhibited different behaviors from one fjord to the next. This complex spatial pattern has been difficult to explain due to a dearth of information about fjord depths and ocean properties in the fjords. Here we use National Aeronautics and Space Administration's Operation Icebridge and Ocean Melting Greenland data to map the depth of fjords and glacier bed topography. We detect glacial valleys several hundreds of meters deeper than previously thought. We find that retreating glaciers stood in deep valleys exposed to warm Atlantic water, whereas stable glaciers are perched on sills away from warm water. These results improve our understanding of the evolution of the glaciers and their impact on mass balance of the ice sheet.
3, Noël et al. , 2016). The MC method also requires a precise knowledge of glacier thinning, which is obtained from sparse tracks of OIB or spaceborne altimetry  (Khan et al. , 2014)  over complex topography. Hence, the uncertainty in ice thickness reconstruction accumulates toward the glacier fronts and is maximal at the ice margin, possibly exceeding hundreds of meters. The 2016 Ocean Melting Greenland (OMG) mission collected multibeam echo sounding (MBES) data in the fjords of SEG. In some fjords, the data extended to the glacier fronts, providing precise constraints on calving front thicknesses. In other fjords, the glacier fronts could not be reached by boat due to the presence of heavy brash ice and iceberg debris in front of the glaciers. In 2016, we conducted an extensive airborne high-resolution gravity survey of key glacier fronts to complement the OMG data and provide observational constraints on the most challenging sectors of Greenland.
This gravity survey builds upon prior efforts at using high-resolution gravity data to resolve bed topography in challenging sectors  (An et al. , 2017). Here we combine the OIB airborne gravity data and OMG MBES data in 10 major fjords occupied by more than 20 outlet glaciers. We constrain a three-dimensional inversion of the gravity observations using the MBES data in the fjord and MC on land to obtain a seamless bed bathymetry mapping across the ice margin. We estimate the uncertainty of the reconstruction and discuss the impact of the results on interpreting the detailed, recent deglaciation history of this part of Greenland.
OIB Gravity data were acquired on a AS350-B3 helicopter by the Sanders Geophysics Airborne Inertially Referenced Gravimeter. Eight flights took place between 27 July 2016 and 9 August 2016 for a total 5,445 km between Narsarsuaq and Kulusuk airports, which were used as gravity references (Figure  1 ). The gravity lines were flown at a line spacing of 1 km, with a ground clearance of 80 m, and at a ground speed of 70 knots. The gravity field is sampled at a frequency of 128 Hz. GPS data were recorded at 10 Hz. We used a Riegl Ⓡ laser altimeter with a single optical laser beam to measure distance to the ground, with a range of 1,500 m, a resolution of 1 cm, and an accuracy of 5 cm at a 3. 3 Hz sampling rate. Eotvos, normal gravity, free-air, static, and level corrections were applied to the data to yield free-air gravity anomalies, which we refer to as "gravity data" in the remainder of the paper.
We merge the modeled and observed gravity by adjusting their mean value within a subregion where the OIB gravity and OMG MBES overlap. Where there is no overlap, we use an open land area to calculate this constant shift (B3, B4, and B9). We allow for a smooth transition between observed and modeled gravity over a length scale of 500 m to 1,500 m depending on the width of the fjord. The inversion is conducted everywhere on grounded ice and extends from the edges of the OMG data to the limit of the OIB gravity survey. The resolution of the gravity data is 750 m, but the results are gridded at a 150 m spacing for consistency with BM3  (Millan et al. , 2017). We calculate an optimal bedrock density as the density that minimizes the root-mean-square error between modeled and observed gravity  (An et al.
Results The inversion combines three data sets in a seamless fashion: (1) OMG in the fjords, (2) BM3 upstream, and (3) OIB gravity data at the ice-ocean transitions. We find minimum densities for B1, B2, B4, B5, B6, and B9 (Figures  1  and S2 ) that are ranging from 2. 5 to 2. 85 g/cm 3 , which are consistent with the density of granodiorite and orthogneiss found in this region  (Henriksen et al. , 2009; Pechnig et al. , 2005). After the inversion, the gravity misfit drops from 8. 1 to 1. 7 mGal (Table  S1 ). Translated into meters, the inversion reduces the uncertainty in bed elevation from ∼140 to ∼30 m. Differences between BM3 and the gravity inversion are >100 m hence reflect net improvements in bed elevation mapping. For five glaciers (Ikertivaq N. N. , M. , S. , KøgeBugt N. , and Eqalorutsit), we observe larger differences in areas where bed mapping with MC did not extend to the coastline and was completed using a krigging interpolation.
In B1, we find a 500 m deep channel in front of Ikertivaq N. N. ( Figures  2a  and 3 ) and a 800 m deep channel for Ikertivaq M. and Ikertivaq S. S. that extends offshore and inland. These deep troughs are not present in BM3. In B2, KøgeBugt C. has an 8 km wide, 600 m deep, relatively flat-based fjord (Figures  2b  and 3 ). In B3, our new bathymetry reveals a 800 m deep fjord in front of Gyldenløve (Figures  2c  and S5 ). The gravity data extends the bathymetry mapping significantly in B4 (A. P. Bernstorff, Figure  2d ), B6 (Mogens and Tingmiarmiut, Figure  2f  ), and B9 (Eqalorutsit and Qajuutaap, Figure  2i ) for the first time (Figures  2  and S8 ). Conversely, in B5, B7 and B8, the OMG coverage already extended to the ice fronts. Inland, our new gravity-derived bed elevation shows significant improvements compared to BM3.
In B3, Graulv has been stable for 80 years despite its ice front being grounded at 400 m deep. We have no CTD measurements near the ice front and no data between the OIB gravity inversion and the OMG campaign (green circle in Figure  1 ). We posit that a sill may exist in the fjord that limits the access of AW. Farther south in B6, BM3 is not compatible with the evolution of Mogens N. because it displays a shallow (200 m depth) prograde bed elevation not exposed to AW. The widespread retreat observed in 2016-2017 in B6 is, however, consistent with our mapping: the ice fronts were dislodged from their stabilizing sills (Figures  3  and S5 ) to retreat along retrograde beds. We note three low retreat anomalies in B3, B8, and B9. In B8, the fjord has a 200 m deep sill that limits the access of warm AW to Anorituup (Figure  S9 ).
This glacier also calves at 8 m/d, which is 1 order of magnitude higher than typical ocean-induced melt rates at ice fronts  (Rignot et al. , 2016). In B3 and B9, we have no CTD or bathymetry data outside of the OIB survey and cannot determine the cause of the stability. We speculate that a sill may be present in the unmapped part of the fjord that limits the access of warm AW to the ice. Overall, except for two glaciers (Qajuuttap Se. and Graulv) for which we need additional data, the new bed map reveals pathways for warm AW to reach glacier fronts. For 17 out of 20 glaciers, we find bed elevations several hundreds of meters deeper than BM3, which makes the glaciers more vulnerable to AW (Figure  S9 ). We have confidence in the results because the derived ice fluxes for 1980s to 1990s are in better agreement with the balance fluxes, our results match available RS data, and the misfit between observed and modeled gravity is lower (Table  S1 ).
Conclusions We present a new bathymetry and bed mapping of southeast Greenland from a 3-D inversion of OIB gravity data over 20 glaciers in 10 major fjords. Our multisensor approach provides the first reliable mapping of these inner fjords and terminal valleys where glaciers have been retreating during the last 80 years. The results reveal deep channels hundreds of meters deeper than estimated previously that provide natural pathways for AW to reach the glacier fronts. The uncertainty in bed elevation has dropped to 30-50 m, or only about 10% of the ice front thickness (versus >50% in BM3). Where we have reliable RS data, we confirm the precision of the inversion. In addition, the ice fluxes from the 1980s are much closer to the balance fluxes, as expected. We also find that the pattern of retreat or advance of nearly all glaciers is consistent with three major factors: (1) the presence of warm AW in the fjords, (2) the existence of retrograde bed, and (3) the presence of stabilizing sills or prograde slopes.
These relations are measured with high precision. In Ref. [55]  it was reported that data provides tentative evidence for the existence of TeV-scale heavy neutrinos. In most of the mass range below the W boson we consider here, the constraints from electroweak precision data are subdominant compared to direct searches. Also observations of the invisible Z decay width are not sensitive to heavy neutrinos that are much lighter than the Z boson because, as a result of the unitarity of the total (light and heavy) neutrino mixing matrix or "GIM suppression", the additional decay channels into N i are exactly compensated by a reduction of the decay amplitude into light neutrinos (as long as M i can be kinematically neglected). However, for masses near or above the W mass, where the constraints from DELPHI and L3 begin to fade (cf.
The Antarctic temperature changes over the past millennia remain more uncertain than in many other continental regions. This has several origins: 1) the number of high resolution ice cores is small, in particular on the Antarctic Plateau and in some coastal areas in East Antarctica; 2) the short instrumental records limit the calibration period for reconstructions and the assessment of the methodologies; 3) the link between isotope records from ice cores and local climate is usually complex and dependent on the spatial and time scales investigated. Here, we use climate model results, pseudoproxy and 5 data assimilation experiments to assess the potential of reconstructing the Antarctic temperature over the last two millennia based on a new database of stable oxygen isotopes in ice cores compiled in the framework of Antarctica2k (Stenni et al.