dataesr/scientific-paragraphs-categorization huggingface dataset:
Mylène Jacquemart was funded by the  Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) research program Climate Change Impacts on Alpine Mass Movements (CCAMM)  and the  Swiss National Science Foundation  (grant no. 200021_184634 ). Shashank Bhushan  was supported by  NASA  ( FINESST award  no. 80NSSC19K1338 ;  Future Investigators in NASA Earth  and  Space Science and Tech- nology). David E. Shean  was supported by  NASA  ( HiMAT-2  award no. 80NSSC20K1595 ;  High Mountain Asia Team ). Review statement. This paper was edited by  Filippo Catani  and reviewed by three anonymous referees.
We determine the bed-separation and the quantity of water stored at the interface between ice and bedrock using the uplift approach. For this purpose, we use the comprehensive data set of in situ measurements performed in the ablation area over 2 years of continuous observations. We observe a strong surface uplift over the winter/spring seasons. We then correct this surface uplift by the influence of seasonal changes in strain rates and sliding velocity using ice-flow modeling, and conclude that the surface uplift observed in winter/spring is mostly caused by an underlying bed separation increase of similar amplitude.
Data Surface ice-flow velocities and point-mass balances were monitored between 2018 and 2020 using a network of ablation stakes (Figure  1 ) composed of 17 stakes  (1, 2, 4-12, and 14-19)  spatially distributed over the selected area and 14 stakes (A-N) aligned along a longitudinal profile in two different sectors, designated as upper (A-G) and lower (H-N) sectors in this paper. The ablation stakes were 10 m long and made of five sections, each two m long, tied together with metal chains. The stakes were replaced each year and were always set up at the same locations, using a hand-held GPS device (accuracy of ±10 m), to ensure comparison. Geodetic measurements were repeatedly performed to obtain the 3D coordinates of the ablation stakes over the monitoring period. For this purpose, we used a Leica 1200 Differential Global Positioning System (GNSS) unit, running with dual frequencies.
resif. fr). The registered GNSS data consist of multi-frequency GPS, GLONASS, and GALILEO signals at 1 s intervals. These data were analyzed in a regional network of 13 fixed reference stations from the RENAG network (http://renag. resif. fr) at distances between 3 and 180 km. Average daily positions have a formal uncertainty of 1 and 5 mm on the horizontal and vertical components, respectively, yielding annual velocity uncertainties ranging between 0. 5 and 2. 5 m. a -1. Theses measurements were started in April 2019 and were maintained until the summer of 2021. Geodetic surveys of the glacier surface were conducted on 5 September 2018, 13 September 2019, and 18 September 2020 using the senseFly eBee + Unmanned Aerial Vehicle to obtain digital elevation models (DEMs). A total of 720 photos in 2018, 673 in 2019, 646 in 2020 were collected with the onboard senseFly S.
O. D. A. camera (20 Mpx RGB sensor with a 28 mm lens from an average altitude of 140 m above the glacier surface). Prior to the survey flights, we collected DGPS measurements of ground control points (GCPs) that consisted of rectangular red pieces of cloth (1. 00 × 0. 60 m) with painted white circles (0. 40 m diameter) on the glacier (10 in 2018, 20 in 2019, and 12 in 2020) and ten 0. 40 m diameter white circles painted on rocks on the ridges of the glacier. The original resolutions of the orthomosaic and DEMs were 0. 10 and 1. 00 m respectively. The images from the survey were processed using the Structure for Motion (SfM) algorithm that is implemented in the software package Agisoft Metashape Professional version 1. 6  (Smith et al. , 2015). The SfM stereo technique was then used to generate a dense point cloud of the glacier surface.
Numerical Modeling Procedure We use numerical modeling to quantify the extent with which the observed uplift may be due to the change in sliding speed on a sloping bed (term Δu b tan β Δt in Equation  2 ) and the change in vertical deformation of ice (term Δ ɛ ̇zz H Δt in Equation  2 ). Our numerical model thus does not include a representation of uplift due to cavitation, which we aim to infer a posteriori from the discrepancies between the predicted and the observed uplift. We use the finite element software Elmer/Ice  (Gagliardini et al. , 2013)  to solve for the coupling between ice flow and basal hydrology. For the ice flow, the Stokes equations are solved without approximation which makes possible the quantification of all components of the strain-rate tensor everywhere in the glacier.
The mass balance model is forced by daily temperature and precipitation taken from the Safran reanalysis  (Vernay et al. , 2021) , and is calibrated from the GLACIOCLIM monitoring network data (https://glacioclim. osug. fr/) over the period 1975-2020 in order to reproduce both spatial and temporal variability of the measured surface mass balance. We assume that surface melting is directly supplied to the glacier bed. The timestep is set to 48 min for the subglacial hydrology and 4 days for the ice dynamics. Sub-daily melting rate used to force the subglacial hydrology model is obtained from hourly temperature time series also available from the Safran reanalysis. The hydro-mechanical model has been calibrated using sliding speed observed in situ over a 20-year period (2000-2020)  (Gilbert et al.
Horizontal winds have been nudged towards the ECMWF meteorological analyses (ERA-Interim) in the online version of the model. Vertical diffusion is parameterized by a local approach of  Louis (1979) , and deep convection processes are parameterized by the scheme of  Tiedtke (1989). The offline model LMDz, coupled with the Simplified Atmospheric Chemistry System (SACS) module  (Pison et al. , 2009) , was previously used to simulate atmospheric mole fractions of trace gases such as CH 4 , carbon monoxide (CO), methyl chloroform (MCF), formaldehyde (CH 2 O), or hydrogen (H 2 ). This system has been recently converted into a chemistry parsing system  (Thanwerdas et al. , 2022). It follows the principle of the chemical parsing system of the regional model CHIMERE  (Mailler et al. , 2017; Menut et al.
Observations Different data sets of observations are either assimilated in our inversions or used to evaluate our simulations and to estimate the impact of the Cl field. (White et al. , 2021). Reported uncertainties are generally below 0. 15 ‰. The station locations and additional information can be found in Fig. S3  and Tables S6 and S7. The analysis of the impact of Cl on CH 4 vertical profiles is conducted using a set of 115 AirCore profiles recovered from 11 different sites over the 2012-2018 period. A total of 80 profiles are provided by the NOAA GML aircraft program  (Baier et al. , 2021; Karion et al. , 2010)  and 35 other profiles by the French AirCore program  (Membrive et al. , 2017). The balloon-borne AirCore technique  (Karion et al. , 2010)  allows air samples to be taken from the stratosphere (up to approximately 30 km) to the ground, upon a parachutebased descent.
Data availability. The data for CH 4 (https://doi. org/10. 15138/VNCZ-M766, Lan et al. , 2022)  and δ 13 C(CH 4 ) observations were downloaded from the NOAA-ESRL server https://www. esrl. noaa. gov/gmd/aftp/data/trace_gases (White et al. , 2021). Data sets for the input emissions were provided by the Global Carbon Project (GCP) team. The Air-Core vertical profiles from the NOAA-ESRL Aircraft Program (https://doi. org/10. 15138/6AV0-MY81; version 20181101;  Baier et al. , 2021)  were provided by Colm Sweeney and Bianca Baier. The Cl-Sherwen and Cl-Wang fields were provided by the corresponding authors of Sherwen et al. (2016b)  and Wang et al. (2021), respectively. The Cl-INCA field, the modeling output files, and the AirCore vertical profiles from the French AirCore Program are available, upon request, from the corresponding author.
13 C solid state NMR and FTIR analysis. 13 C solid state NMR analysis was performed using a Bruker Avance III HD 300 MHz spectrometer (Bruker Corp. , Billerica, MA, USA). A classic (Bruker CP) cross polarization sequence was used at magic angle spinning. The samples were spun in 4-mm zirconia rotors, closed with KeI-F caps, at 12. 5 kHz spinning frequency on 4-mm MAS Bruker probe operating. Cross polarization from 1 H to  13  C was performed using the Bruker CP method, where the contact time was set at 2 ms, the number of scans was 2048, and D1 was 5 s. Spinal-64 heteronuclear decoupling was applied during the 15-ms acquisition time. The decoupling pulse was optimised on a  13  C-labelled histidine standard sample and set to 6. 4 μs. The NMR data were processed (phase and baseline) by top spin Bruker 4.
Statistical analysis. Statistical analysis was performed using SAS 9. 4 analytical software (SAS Institute Inc. , Cary, NY, USA). The gas production data set was scaled down to a 0. 5 h resolution and non-linear regression analysis was performed using the MODEL procedure and Gompertz function  31. Significant differences in estimated parameters of gas production kinetics among the substrates or treatments were assessed using likelihood ratio 95% confidence intervals. The concentration of peNDF 8 as well as the concentrations of CO 2 , CH 4 , and H 2 were compared among the three TMR variants using the Kruskal-Wallis test with the NPAR1WAY procedure. In the gas components data set, outliers were identified using boxplots and removed. Outliers were defined as observations further than 3 times of the interquartile range.
2e ). Then, digestibility, calculated as 1 -b, of aNDFom was 0. 68, 0. 74, and 0. 79, digestibility of ADFom was 0. 58, 0. 57, and 0. 65, and digestibility of ADL was 0. 02, 0. 13, and 0. 08 in TMR composed with untreated straw, NaOH treated straw, and urea treated straw, respectively. The results of this regression analysis can be interpreted as the true digestibility coefficients of the fibre fractions  57. They may differ from the apparent digestibility coefficients we obtained from the digestibility trial, however, notable endogenous losses of fibrous carbohydrates are not to expect. The number of available data points, their distribution, and range are decisive for the reliability of the regression approach and must be critically examined in the present case. 13 C NMR and FTIR spectra.
We used satellite telemetry which has been shown to be highly suitable to study migratory movements and spatial-temporal patterns of red-throated divers within and between years  (Schmutz 2014 , Paruk et al. 2015 , Spiegel et al. 2017 , McCloskey et al. 2018). Additionally, we used carbon stable isotope analyses of neck feathers in combination with satellite tracking data to infer moulting areas used prior to capture. Stable isotope values of a predator are related to those of its prey and the area where the predator foraged. Stable isotope values of prey vary with its trophic position and geographic region. Nitrogen stable isotope values (δ 15 N) increase with trophic position, while carbon stable isotope values (δ 13 C) depend more on the carbon uptake by the primary producer and thus differs among habitats  (Peterson and Frey 1987 , Frey 1988 , Hobson 1999 , Cherel and Hobson 2007).
Data filtering The tracking data were filtered to reduce noise from location fixes with low or unknown accuracy. Filtering followed the approach in  Dorsch et al. (2019) ,  Burger et al. (2019), and Heinänen et al. (2020)  and was conducted using the package 'argosfilter'  (Freitas et al. 2008 ) in R (R CORE TEAM 2018). First, the sdafilter Filter (lat, lon, dtime, lc, vmax = 20) was applied. Then all locations with unrealistic swimming/flying speeds were removed, using the McConnell algorithm  (McConell et al. 1992 ), unless the point was located at less than 5 km from the previous location. Secondly, ArcGISv. 10. 1 (ESRI 2012)  was used to further inspect the filtered dataset and any remaining obvious outliers, such as unrealistic positions were removed from the dataset. Finally, positions recorded during the first two weeks after the transmitter implantation were excluded from the data set due to possible impacts of capture and surgery on the behaviour during recovery period.
Data collection and definition of terms and seasons We used the migratory pattern observed in this dataset to define seasons within the annual cycle (Fig. 1 , Table  1 ). The timing of site use varied from individual to individual and from year to year (Fig. 1 ). Therefore, we decided to define each season by the months in which at least one diver showed activity consistent with that season (i. e. migratory movements, or settlement during breeding, moulting or wintering season, Table  1 ). After spending some time on inland lakes, presumably for nesting, some red-throated divers moved to adjacent marine waters. Depending on the time period they spent for nesting, these individuals are likely failed breeders or non-breeders. We did not consider these as staging periods as long as the diver stayed in the presumed breeding area.
Analysing tracking data PTTs were deployed over three separate years so ordinal date (day of the year) was used as the temporal variable allowing comparisons to be made across years. We used ArcGISv. 10. 1 (ESRI 2012), QGISv2. 18 (QGIS Development Team 2018) and two projections (Lambert Azimuthal Equal Area projection: ETRS89 / ETRS-LAEA (EPSG: 3035), North Pole Azimuthal Equidistant projection (EPSG: 102016)) to inspect migratory patterns and to quantify migratory movements. We calculated migratory distances between capture and breeding sites by summing the length of all vectors created from point to point of the PTT locations from the first day of departure from a site to first day of arrival at the new site. We did not include movements within staging areas and sites into the distance calculation.
In addition, we assessed the strength of connectivity by calculating the Mantel correlation coefficient within the R package ade4  (Dray and Dufour 2007 , Ambrosini et al. 2009 , Trierweiler et al. 2014 , Cohen et al. 2018). Statistical significance was determined using 9999 permutations  (Trierweiler et al. 2014 , Ambrosini et al. 2009 ). The Mantel correlation coefficient (rM) was calculated between pairwise (orthodromic) distance matrices of (  i  We compared home range estimates for an overlapping site use during moult and winter (after the first breeding season) and between individuals from shared and non-shared breeding regions within the same season. We calculated 95% and 50% kernel density contours using the adehabitatHR package  (Calenge 2011 ) in R, using h="LSCV/h-ref" as the smoothing parameter.
We used only data from individuals that covered a full season and only one position of the best location class per day to avoid overrepresentation of some intervals. To calculate sizes of home ranges and core areas in km 2 we converted the area of kernels to UTM units and created a new kernel (95% and 50%) based on the UTM coordinates. The UTM-zone was chosen individually depending in which zone the estimated area was located using the WGS1984 datum. We furthermore used these kernel density estimates to compare consistency in site use and a potential spatial overlap of individual home ranges between two consecutive years. When the same time period during winter (n=4) and moult (n=1) in consecutive years was available, 50 % and 95 % density contours were calculated. Consistency and flexibility of individual migratory movements, phenology and site utilisation between the two years was calculated using an ANOVA based repeatability index (also called the intraclass correlation coefficient R) as an agreement of measurements between consecutive years  (Nakagawa and Schielzeth 2010).
We used a sub-sample set of individual data (n=10) provided by the satellite transmitters and determined the moult location of each bird to relate the isotopic information of feathers to geographic regions (North Sea vs. Baltic Sea). Then we used these values to assign moult locations for the remaining birds tracked with satellite transmitters (n=9) and for birds where no information from tracking data was available (n= 14). The feathers we sampled from satellite tracked birds were grown in the year before and thus tracking data collected in this study did not include the time when the sampled feathers were grown. Therefore, additionally literature of isotope values in the North and Baltic Sea were incorporated to confirm the classification. We revised carbon isotope values from muscle, eggshells and feathers of piscivorous vertebrates whose diets overlap with that of the red-throated divers  (Das et al 2004 , Céline Mahfouz et al.
2017 , Corman et al 2018 , Kleinschmidt et al. 2019 , St John Glew et al. 2019 , Christie 2021 , Table  2 ) to obtain information on differences in carbon stable isotope values between the two seas. Stable isotope values were finally compared (North Sea vs. Baltic Sea) using the Wilks' Lambda test and a One-way MANOVA (Bartlett Chi 2 ) and the package rrcov  (Toderov and Filzmoser 2009)  to reveal if they statistically differ. The isotope data were used to assign pre-capture moult locations to all birds tracked in this study (n=33). When information about utilised moulting sites after capture was given by the tracking data (n=19) it could be compared with the moulting sites assigned by isotope data. Therefore, moulting areas could be identified on a broad scale throughout the year and between years.
4 ). In autumn, on the way back from the breeding areas, most red-throated divers followed the directions and pathways they have already used during the spring migration with 77% of red-throated divers performing a step-wise migration with separate moult and winter migrations (Fig. 2 , Fig. 6 ). All birds moulting in the North Sea according to tracking data had δ 13 C > -18‰, while birds that moulted in the Baltic Sea had δ 13 C < -18‰. This threshold was confirmed by the literature values. We thus applied this classification to all birds tracked through the annual cycle (n=33) and assigned moulting regions in the Baltic Sea and the North Sea to a similar number (51% and 49%, respectively). The isotope values from red-throated divers probably indicated a split between moulting in North Sea and Baltic Sea (Fig.
Discussion Based on tracking data and stable isotope analyses we obtained a comprehensive dataset on annual movements of NW-European red-throated divers that addressed our hypotheses as outlined below. Tracking data lasted for up to two consecutive years and thus allowed to assign individual site utilisation within and between years. Stable isotope analyses added information about moulting sites where no tracking data were available. Isotopic values in our study seem to be clearly separable between North and Baltic Sea and in line with the locations determined by the tracking data. Although matching of isotope data and tracking data without temporal overlap may have some uncertainties, published isotopic values from the North and Baltic Sea backed up our assignment of moulting sites. This approach has previously been used successfully by  Oppel and Powell (2008)  to determine winter locations of eiders (Somateria spectabilis).
However, in this context results from studies on red-throated diver distributions and displacement effects in the eastern German Bight showed that red-throated divers remained in the general area, but shifted their distribution and congregated outside disturbed areas  (Mendel et al. 2019 , Vilela et al. 2021). Mendel et al. (2019)  analysed long term datasets of aerial and ship based surveys, whereas  Vilela et al. (2021)  analysed long term datasets of aerial surveys. However, no population decline was observed  (Vilela et al. 2021). Considering this, our data based on the visualised tracks, single home ranges (Fig. 6 ) and statistical analyses of individual repeatability (Table  A1 ) show an individual consistent site use in consecutive years, considered over a broad scale. Combined with the findings of  Mendel et al.
Acknowledgements We thank  G. Guillou  from the  Plateforme Analyses Isotopiques of LIENSs laboratory  for running the stable isotope analyses. Thanks are due to the  CPER (Contrat de Projet Etat-Région)  and the  FEDER (Fonds Européen de Développement Régional)  for funding the  IRMS of LIENSs laboratory. The  Institut Universitaire de France (IUF)  is acknowledged for its support to PB as a Senior Member. Many thanks to  Nadja Küpper  and  Yvonne Schumm  for helpful discussion and support with the statistics. We greatly thank everybody involved in the capture of divers for their support. Thomas Grünkorn  and  Jorg Welcker  completed our field team and supplied valuable support. Thomas Grünkorn also contributed by ringing of the captured red-throated divers. Sören Zenner  ( OS-Energy ) allowed flexible organisation and provision of ships during the field seasons and we thank the ship captains and crews of the  MS Madog and MS Arctic Hunter  for their reliability and support during fieldwork.
These data cannot be assumed representative of the whole fisheries' bycatch events. This feature makes it difficult to use classic ratio estimators since they require a truly randomised sample of the fishery by dedicated observers. We applied a newly developed approach, regularised multilevel regression with post-stratification, to estimate total bycatch from unrepresentative samples and total fishing effort. The latter is needed for post-stratification and the former is analysed in a Bayesian framework with multilevel regression to regularise and better predict bycatch risk. We estimated the number of bycaught dolphins for each week and 10 International Council for the Exploration of the Sea (ICES) divisions from 2004 to 2020 by estimating jointly bycatch risk, haul duration, and the number of hauls per days at sea (DaS).
Bycatch risk in pair trawlers flying the French flag was the highest in winter 2017 and 2019 and was associated with the longest haul durations. ICES divisions 8. a and 8. b (shelf part of the Bay of Biscay) were estimated to have the highest common dolphin bycatch. Our results were consistent with independent estimates of common dolphin bycatch from strandings. Our method show cases how non-representative observer data can nevertheless be analysed to estimate fishing duration, bycatch risk and, ultimately, the.
Because of these data quality issues,  Authier et al. (2021)  conducted a simulation study to gauge the potential of investigating recent methods for the analysis of nonrepresentative samples (for a recent example of a model-based approach to estimate bycatch, refer to  Luck et al. , 2020)  in the context of small cetacean bycatch: they concluded the potential of regularised multilevel regression with post-stratification to infer more accurately bycatch rates (although uncertainties remained large). The approach of  Authier et al. (2021)  also makes use of Gaussian process priors but does not necessarily assume that a large dataset has been collected. We analysed historical bycatch monitoring data collected by onboard observers (from 2004 to 2020) on PTM, a métier historically associated with high levels of dolphin bycatch in the Bay of Biscay  (ICES, 2019; Murphy et al.
, 2021). Leveraging recent modelling developments (see companion article;  Authier et al. , 2021) , we jointly estimated bycatch risk, haul duration, and number of hauls per days at sea (DaS) from an updated and revised observer dataset on common dolphin bycatch. The modelling procedure accounts for the sparseness of the bycatch incident dataset and the low observer coverage through constraints. This type of constraint (which can be viewed as some sort of penalisation) is also called regularisation. We used structured priors, such as Gaussian processes, to achieve regularisation and leverage the within-year information at the weekly scale (inducing correlation between some weeks). Structured priors allow inducing some spatial-or temporal-dependency between so called random-effects whereas unstructured priors do not induce such dependency (but both assume exchangeability).