dataesr/scientific-paragraphs-categorization huggingface dataset:
0-ice model is not possible for reasons of computational cost. Moreover, neither the Antarctic nor Greenland ice sheets were likely in static equilibrium states during the pre-industrial period, and reliable observational datasets for model evaluation in Antarctic regions are not available prior to the satellite era. Initialisation of coupled climate-ice sheet models is an area of active research (e. g. Lofverstrom et al. , 2020) , and there is no consensus on best practice. The simulations described in this paper grew out of model development efforts and were initialised in a rather ad hoc manner which will be improved in future research. The topic of coupled ice-climate initialisation is a challenging research question, and much work is still required to develop best practice in this area.
Stage A: ice sheet model inversion An AIS initial state close to the present day was derived in  Cornford et al. (2016). Fields for the basal traction and stiffening coefficients were determined by the solution of an ice sheet inversion procedure  (Cornford et al. , 2015)  such that ice velocities in BISICLES closely match surface observations  (Rignot et al. , 2011). The inversion is done using the stress balance equation keeping the ice sheet geometry fixed; hence basal melting and accumulation play no explicit role in it. An implied mass balance (SMB -basal melting) for this state can be diagnosed from the divergence of the model velocities. The bedrock elevation and ice thickness in this inversion were constructed from the Bedmap2 dataset  (Fretwell et al. , 2013) , and the bedrock was modified  (Nias et al.
Results In this section we describe the results of the SSP1-1. 9 and SSP5-8. 5 simulations, concentrating on the factors important for the ice sheet mass balance. Section 3. 1 evaluates the initial evolution of the ensemble against some modern observational datasets, followed by sections which detail projection of changes to 2100 in the two climate change scenarios. Section 3. 2 analyses changes in ice shelf basal melting, while Sect. 3. 3 analyses SMB. Throughout, the terms SSP1-EM, SSP5-EM and ALL-EM refer to the ensemble means of the SSP1-1. 9 ensemble, SSP5-8. 5 ensemble and all simulations, respectively.
While recent observations are broadly available for temperature and salinity (Fig. 3e  and f ), there are no complete observational datasets for the barotropic stream functions and mixed-layer depth around the southern polar region. Figure  3a  demonstrates the ALL-EM of the simulated 2020-2030 barotropic streamfunction south of 55 • S, which resembles the observed Antarctic Circumpolar Current path around Antarctica  (Sokolov and Rintoul, 2009)  and shows that the model simulates the existence and strength of the Ross, Weddell and Australian-Antarctic subpolar gyres as reported by many coupled climate models  (Wang and Meredith, 2008). The ALL-EM of continental shelf temperature (Fig. 3c ) captures the general pattern of cold shelf water in the Ross and Weddell seas and warm water in the Amundsen and Bellingshausen seas compared to the World Ocean Atlas 2009 climatology (Fig.
(2013)  and  Adusumilli et al. (2020)  as Obs-Rig and Obs-Ad, respectively. Since the uncertainties in the observational datasets are very large and the disagreement between two datasets is also large, we do not perform detailed statistical analysis on them and compare the interval estimates only. In this loose comparison, the ALL-EM of areaintegral melt flux across Antarctica generally agrees with the observations. The simulated total melt flux over all Antarctic ice shelves (981. 8 ± 80. 5 Gt yr -1 ) is within the range of Obs-Ad although slightly below the lower end of Obs-Rig. In the warm Amundsen region, the basal melt fluxes under the Pine Island Glacier (PIG) and Thwaites ice shelves coincide with the Obs-Rig and the Obs-Ad range, respectively. Under the large Ross and Filcher-Ronne ice shelves, we simulate melt fluxes that fall within the observed ranges of Obs-Ad where their mean values are very close to each other.
Acknowledgements. The authors would like to acknowledge  Stephen Cornford  for giving the required knowledge and information about various things related to the BISICLES model and the UKESM core development team on whose work this model builds. The authors thank  Susheel Adusumilli  and  Rob Arthern  for providing the observation data used here for comparison. Model simulations were carried out on the Monsoon and  NEXCS (National Environmental Research Council  share of XCS) collaborative highperformance computing facilities funded by the  Met Office  and the  Natural Environment Research Council. The authors would also like to acknowledge the constructive comments and helpful suggestions of our reviewers and editor, who helped to shape and improve this paper. Financial support. This research has been supported by the  UK Research and Innovation  (grant nos.
We present the third and final data release of the K2 Galactic Archaeology Program (K2 GAP) for Campaigns C1-C8 and C10-C18. We provide asteroseismic radius and mass coefficients, κ R and κ M , for ∼ 19, 000 red giant stars, which translate directly to radius and mass given a temperature. As such, K2 GAP DR3 represents the largest asteroseismic sample in the literature to date. K2 GAP DR3 stellar parameters are calibrated to be on an absolute parallactic scale based on Gaia DR2, with red giant branch and red clump evolutionary state classifications provided via a machine-learning approach. Combining these stellar parameters with GALAH DR3 spectroscopy, we determine asteroseismic ages with precisions of ∼ 20 -30% and compare age-abundance relations to Galactic chemical evolution models among both low-and high-α populations for α, light, iron-peak, and neutron-capture elements.
14) + 0. 256e 2(J-Ks) , which is a relation introduced by  (De Silva et al. 2015). The targets were prioritized for the most part by ranking targets in order of brightest to faintest visual magnitude, with higher priority given to targets selected based on spectroscopy. The majority of the targeted stars were observed by K2, and follow the target selection functions, with a few exceptions. Notably, the priorities of C7 targets were mistakenly reversed during the Kepler office target list consolidation. For details of the effects of this on the C7 selection function, see  Zinn et al. (2020)  and  Sharma et al. (2019) ; the selection functions for all the campaigns are described in S. Sharma et al. , in prep. In addition, module 4 failed while taking data in C10. We therefore exclude data from this module in C10 because of the short duration of data collection before the failure; modules 3 and 7 had already failed by the time the K2 mission began, and so there are no data for these modules in K2 GAP.
We report in Table  5  the median fractional uncertainties in ν max , ∆ν , κ R , and κ M for both RGB stars and RC stars, and which may be considered typical of the uncertainties in our sample. We also include typical fractional uncertainties in these parameters from K2 GAP DR2, APOKASC-2  (Pinsonneault et al. 2018) , and another, independent analysis of Kepler data  (Yu et al. 2018). The typical ∆ν uncertainty for K2 GAP DR3 is somewhat larger than it was in K2 GAP DR2 due to the previously mentioned difference in how sigma clipping is performed in the averaging procedure used in the two data releases. The resulting precisions in RGB masses, which are determinative in asteroseismic age precisions, are about a factor of two larger than those of Kepler, corresponding to uncertainties of about 20 -30% in age.
Injection tests In the previous section, we detailed the dependence of asteroseismic results across pipelines. However, there are likely additional systematics due to the length of the K2 light curves compared to, e. g. , Kepler light curves. Indeed,  Hekker et al. (2012)  revealed non-negligible variations in completeness, precision, and accuracy in red giant asteroseismic parameters due to the length of the time series (i. e. , the time baseline). In order to test the completeness, precision, and accuracy of the different asteroseismic modelling pipelines for K2-like data, we generated synthetic data for which we knew the "true" ν max and ∆ν from Kepler and performed blind injection recovery tests. We first created a grid in magnitude-ν max space from the distribution of Kepler stars from APOKASC  (Pinsonneault et al.
2018) ; the faint giant sample of  Mathur et al. (2016) ; and the M-giant sample of  Stello et al. (2014) , in order to select Kepler stars evenly across this parameter space. From each bin, when possible, we generated K2-like light curves based on 80d segments of Kepler light curves via two methods. First, we attempted to select from each bin three Kepler stars with at least five quarters of data each, from which we created 15 synthetic K2 light curves (selecting five different 80d sections from three stars). Second, we attempted to generate 15 synthetic K2 light curves from 15 different Kepler stars from a single 80d section of each of their light curves; in practice, not each bin had enough stars to create 30 synthetic K2 light curves from these two methods. Each of these synthetic K2 light curves was created using KASOC v1 Q1-Q14 light curves (Handberg & Lund 2014), linearly interpolating the Kepler flux onto a cadence of a star in K2 C3 to mimic the spectral window of actual C3 data and the frequency resolution of K2.
We show in Figure  4a  the SNR of the synthetic sample. 7 We compute the SNR of the synthetic K2 data in a way that takes into account both the expected maximum mode amplitude and the granulation background level at ν max. To do so, we adopt the approach from Campante et al. (  2016 ), assuming 3 modes per order, ignoring observation integration time effects, and assuming a noise level according to the observed star-to-star white noise level at high frequencies in the spectra. For the mode maximum amplitude, we adopt the model M 4,β from  Corsaro et al. (2013). The points are colored by the provenance of the Kepler data, where there are potentially multiple synthetic stars per KIC ID because of the division of the Kepler light curves into 80d sections. In total, there are 57 synthetic stars from the M-giant catalogue  (Stello et al.
This suggests that there are non-negligible systematics in asteroseismic pipeline recovery that are a function of baseline, and which would result in too-small radii and masses compared to Kepler asteroseismology (see the below comparison between mass distributions in K2 and Kepler). Time baseline seems to have the smallest impact on ∆ν, since several pipelines report nearly identical ∆ν with Kepler as with K2 data (though some pipelines show substantial disagreement). ν max , however, suffers from significant biases based on the time baseline: excursions of a 2 -3% and zero-point biases of 1 -2% are observed. There are also indications that some pipelines may have SNR-dependent biases, which manifest as trends in the fractional agreement between Kepler and synthetic K2 values as a function of SNR in Figure  6.
Apart from testing the ν max -, ∆ν-SNR-and time baseline-dependent biases in pipeline results, we can also test the internal consistency of the uncertainties using the synthetic K2 data. Since we have results from precise Kepler data, we can compare it to the less precise, simulated K2 data for the same stars, and evaluate if pipeline results are internally consistent to within their reported uncertainties. To do so, the observed distribution of the fractional deviation between the K2 and the Kepler measurements ('true' in Fig. 7 ) is compared to the expected distribution ('reported' in Figure  7 ), created by drawing Gaussian random variables assuming the reported K2 uncertainty for each simulated K2 star. If the reported uncertainties were self-consistent, then the two distributions would be identical.
First, the right edge of the clump is better defined in Kepler data by having better precision and more high-mass secondary red clump stars. The Kepler sample also extends to higher frequencies than does K2 GAP DR3, presumably due to better noise properties in Kepler compared to K2. However, K2 has double the fraction of low-frequency (< 20µHz) oscillators than does Kepler, in spite of the tendency to not recover stars in this frequency regime with K2-like time baselines (Fig. 12 ). Note that the overall shift in mass between the  Yu et al. (2018)  and K2 GAP DR3 samples is consistent with the time baseline systematics in ν max (Fig. 5 ), such that the SYD Kepler ν max values would be expected to be larger by ∼ 1% than K2 ν max values. As with Figure  16a , Figure  17  shows the K2 GAP DR3 stars in the mass proxy v.
CONCLUSIONS The K2 GAP DR3 sample, as the largest asteroseismic sample published to date, and probing a range of Galactic environment, represents an important tool for Galactic archaeology and stellar physics. With 18821 total radius and mass coefficients for red giant branch (RGB) and red clump (RC) stars delivered as part of this final data release, below are our main results. 1. We calibrated our asteroseismic values to be on the Gaia parallactic scale. The radius and mass coefficients, κ R and κ M , that are released in K2 GAP DR3 need only be multiplied by a temperaturedependent factor according to the user's preferred temperatures to yield radii and masses. The typical uncertainties in these coefficients are 2. 9% (stat. ) ± 0. 1% (syst. ) & 6. 7% (stat. ) ± 0. 3% (syst. ) in κ R & κ M for RGB stars and 4.
Our findings also suggest mass-dependent Ba yields, in support of indications from D'  Orazi et al. (2009). Studies of Galactic chemical evolution stand to benefit enormously from a continued focus on considering age and not just stellar abundances themselves, as we have shown here. Indeed, ages are of crucial importance in interpreting chemo-kinematic relations  (Minchev et al. 2019 ) -particularly ages with the level of precision reported here (e. g. ,  Martig et al. 2014). As the largest asteroseismic dataset in the literature, K2 GAP DR3 will prove useful not only for Galactic studies, but also for testing stellar models using the sample's evolutionary state classifications coupled with its accurate and precise asteroseismic masses and radii. ∆ν is adjusted using theoretically-motivated correction factors, X Sharma  (Sharma et al.
ACKNOWLEDGMENTS We would like to thank the anonymous referee whose comments significantly improved the manuscript. JCZ is supported by an  NSF   Astronomy and Astrophysics Postdoctoral Fellowship  under award AST-2001869. JCZ and MHP acknowledge support from  NASA  grants  80NSSC18K0391  and  NNX17AJ40G. YE and CJ acknowledge the support of the  UK Science and Technology Facilities Council (STFC). SM acknowledges support from the  Spanish Ministry of Science and Innovation with the Ramon y Cajal  fellowship number  RYC-2015-17697  and the grant number  PID2019-107187GB-I00. RAG acknowledges funding received from the  PLATO CNES  grant. CK acknowledges funding from the  UK Science and Technology Facility Council (STFC)  through grants  ST/M000958/1  and  ST/R000905/1 , and  ST/V000632/1. Funding for the  Stellar Astrophysics Centre (SAC)  is provided by The  Danish National Research Foundation  (Grant agreement no.
DNRF106 ). The  K2 Galactic Archaeology Program  is supported by the  National Aeronautics and Space Administration  under Grant  NNX16AJ17G  issued through the  K2 Guest Observer Program. This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the University of Mas-sachusetts and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the  NASA Science Mission directorate. Parts of this research were supported by the  Australian Research Council   Centre of Excellence for All Sky Astrophysics in 3 Dimensions  ( ASTRO 3D ), through project number  CE170100013.
This research was partially conducted during the Ex-ostar19 program at the  Kavli Institute for Theoretical Physics at UC Santa Barbara , which was supported in part by the  National Science Foundation  under grant No. NSF PHY-1748958. Based in part on data obtained at Siding Spring Observatory via GALAH. We acknowledge the traditional owners of the land on which the AAT stands, the Gamilaraay people, and pay our respects to elders past and present. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institu-.
Data acquisition We use three types of datasets: rainfall, sediment yield and soil temperature. Monthly and annual rainfall values are obtained by summing the detailed event records measured with a tipping-bucket rain gauge located at the outlet of the Laval catchment (Fig. 1C ). Sediment yield is measured at the outlets of both the Laval and Moulin catchments, where hydro-sedimentary https://doi. org/10. 5194/esurf-2021-49 Preprint. Discussion started: 15 July 2021 c Author(s) 2021. CC BY 4. 0 License. stations have been set up to monitor water discharge and both suspended load and bedload. Suspended sediment concentration is measured with automatic samplers and turbidimeters and suspended sediment flux is computed as the product of discharge and concentration, cumulated over a flood to obtain an event-scale yield.
Bedload volumes are measured after each flood by topographic surveys of a sediment trap located upstream of the station. Bedload volume is then converted into mass using a density of 1700 kg/m 3 , constrained by measurements in the sediment trap  (Mathys, 2006). Thus, the raw data correspond to event-scale values and the total sediment export used in this study refers to the monthly or yearly sum of the suspended load and bedload contributions. For a few flood events, the suspended-load data is missing. In such cases, we reconstructed the event-scale suspended sediment yield based on the average proportions of suspended load and bedload, computed from multiple complete years of total load records. The proportions of suspended load and bedload in the Laval catchment are 74% / 26% for summer floods (May to September) and 57% / 43% for winter floods (October to April).
Soil temperature has been recorded using several PT100 soil-temperature probes located on opposite slopes in an inner meander of the Moulin Creek between August 2005 and December 2019 (Fig. 1C, D ). The acquisition frequency was 10 minutes. We use the data from probes located in bare black marls at uphill and downhill locations on north-and south-facing slopes, respectively (Fig. 1D ), in order to explore variations in soil temperature due to differences in exposure  (Rovéra and Robert, 2005). At each location, four probes are available to measure soil temperature at depths of 1, 6, 12 and 24 cm, respectively. As our interest is focused on frost weathering, we specifically analysed soil temperatures during the winter season, from October 18 th to March 31 st. This period was chosen because (1) negative soil temperatures are almost absent outside these dates; (2) some series miss temperature data for early October.
The soil-temperature dataset includes periods of missing or spurious data, due to probe malfunction, unearthing or burial. The relatively mobile nature of the marls leads to frequent displacement of probes, identified in the temperature records by similar temperatures between probes at different depths or anomalous daily temperature ranges (Fig. 2 ). When these periods are longer than fifteen consecutive days, the full season of data is rejected  (i. e. , 2005/2006, 2010/2011 and 2012/2013). When the missing period is shorter (for seasons 2011/2012 and 2014/2015), we searched for a relation between soil temperature and air temperature (recorded at the closest weather station) in order to reconstruct soil temperature during the missing time interval. We found that a simple two-tier linear fit predicted soil temperature well for air temperature below 10 ºC and soil temperature below 5 °C, with an inflexion point around -4°C.
The soil-temperature gradient was computed between the probes located at 1 cm and 24 cm depth. We summed temperature gradients during all time intervals when the surface temperature (probe at 1-cm depth) was within the frost-cracking window (between -3 and -8 ºC) over a winter cycle, and used this number as a proxy for the frost-cracking intensity. Following  Anderson et al. (2013) , we expressed the FCII in units of temperature-time/length (ºC min/cm in our case). The probes at 24 cm depth on the north-facing slope did not produce reliable data for almost all years considered; we therefore only computed FCII for the south-facing slope. The second indicator counts the number of freeze/thaw cycles as a proxy for the efficiency of frost weathering by volume expansion. The raw temperature data include many insignificant temperature fluctuations around 0 °C, over very short periods that do not allow the formation of frost.
Conclusions Based on our analysis of sediment-yield records and soil-temperature data from the Draix-Bléone CZO and accounting for the inevitable uncertainties in our dataset, we show that frost-weathering processes modulate sediment export by controlling sediment production in these marly catchments. Our main conclusions are summarized below:  Monthly total sediment export (suspended load and bedload) is highly variable and shows a seasonal pattern. The annual hysteresis cycle (Fig. 4 ) shows an anti-clockwise pattern during spring and a clockwise pattern later in the year, suggesting an initial transport-limited regime followed by a supply-limited regime in these catchments.  Total annual sediment export is well correlated with rainfall above an intensity threshold; a threshold of 50 mm/h maximises the correlation (Fig.
Acknowledgements This research has been supported by the  French National Research Agency (ANR)  under the grant  ANR-18-CE01-0019-01  ( DEAR  project) and  INRAE Grenoble. This study was carried out in the  Draix-Bléone Observatory (France)  and used its infrastructure and data. The  Draix-Bléone Observatory  is funded by  INRAE (National Research Institute for Agriculture, Food and Environment) ,  INSU (National Institute of Sciences of the Universe)  and  OSUG (Grenoble Observatory of Sciences of the Universe)  and is part of the  French network of Critical Zone Observatories OZCAR , which is supported by the  French Ministry of Research, French Research Institutions and Universities.
2018). We apply our recently implemented modules for solving the time-dependent equations of radiation-hydrodynamics (RHD) in two and three dimensions  (Moens et al. 2022 ). This work is thus a direct follow-up to the 1D RHD models of WR winds presented in  Moens et al. (2022). However, rather than assuming the ad hoc boosts of the line force that were inherent to previous 1D models (see above), we now use the "Munich atomic database"  (Pauldrach et al. 1998 (Pauldrach et al. , 2001;; Puls et al. 2000)  to also compute a self-consistent line opacity  (Poniatowski et al. 2022). Section 2 describes the applied prescription and setup for the RHD simulations, as well as the hybrid opacity model used to describe the interaction between gas and radiation. In Sect. 3, we present the general properties of our 2D and 3D models.