dataesr/scientific-paragraphs-categorization huggingface dataset:
The classification scheme and accompanying database outlined here is a critical part of this. The classification scheme is a simplification of what were complex systems of land use, but such simplification is necessarily for global-scale aggregation. By coding some variables (domesticates, burning, tillage, etc. ) apart from the land use categories, we attempt to both capture commonly-recorded archaeological variables significant to environmental change and also to keep open the possibility of analyzing associations among classes and variables. For example, raised fields are often found in areas under maize cultivation, but our data structure allows this association to be tested and for new combinations to be identified if they exist. We thus highlight the value of this exercise to archaeology and history as well as to earth system science.
Archaeological analyses of past land use rely on a very large range of indicators, and data coverage and quality vary significantly across space and time. Our database is designed to be public, iterative, and correctable, able to integrate new data and understandings. As a first step in the harmonization of archaeological land use data, it is necessarily preliminary. Although we begin with a limited number of time slices, there is the potential for more powerful transient time series analyses in the future. The LandCover6k research will be able to identify areas or times of rapid land use change, areas of contention that require further work, and areas that are lacking data but clearly are importantly for land use and land cover research. Applied research and modeling groups associated with ecosystems services such as ARIES (Artificial Intelligence for Ecosystems Services) will also benefit from the LandCover6k work, and from the consistent language used in this classification scheme.
We have developed a common language for land use classification, a database for recording land use assessments, and strategies for data management and coordination as a first step toward using these important but scattered, uneven, and regionally-focused historical archives to contribute to a better understanding of the earth system. Figs 2-7 were created using Biorender. com. The authors are grateful to Ka Ki Jacqueline "Jacky" Chan for her help in creating  Fig 9.
Acknowledgments This study was undertaken as part of the  Past Global Changes (PAGES)  project (and its working group LandCover6k). The classification presented here is the result of extensive discussion, argument, and compromise. Archaeological and historical synthesis of this type, and on this scale, is unprecedented, and we acknowledge the range and depth of intellectual contributions to this work. We would like to thank in particular:  Jonathan Aleru ,  William Balee ,  Tilman Baum ,  Carla Jaimes Betancourt ,  Amy Bogaard ,  Michelle Chaput. Joanne Clarke ,  Charles R. Clement ,  Anne Casile ,  Anne Dallmeyer ,  Bamidele Sunday Dennis ,  William Doolittle ,  Anneli Ekblom ,  Laure Emperaire ,  Xiuqi Fang ,  Dan Gavin ,  Amy Goldberg ,  Jane Humphries ,  Nestor Ignacio Gasparri ,  JoseIriarte ,  Christian Isendahl ,  Nadim Jayaraju ,  Nadia Khalaf ,  Carla Klehm ,  Sjoerd Kluiving ,  Julian Laabs ,  Terri Lacourse ,  Emma Loftus ,  Munyaradzi Manyanga ,  Katie Manning ,  Andrew Martindale ,  Nicolas Maughan ,  Sathaporn Monprapussorn ,  Claide P.
S. National Science Foundation ,  Swiss Academy of Sciences  and the  Chinese Academy of Sciences. The work of globalscale coordination, database development, and the work of land use groups around the world was supported with workshop grants from  Past Global Change (PAGES)  and the  Human and Biosphere Commission of INQUA  (the  Global Holocene Land Use -HoLa -International Focus Group  and related projects). Additional funding was provided by the  University of Chicago , the  University of Pennsylvania , and the many institutional and personal sources of support for travel and time that allowed the many participants in this project to contribute their expertise and enthusiasm to this critical effort. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The Kepler data provide oscillation frequency spectra of exquisite quality and resolution, allowing precise estimates to be made of the frequencies of modes of different angular degree (radial = 0, dipolar = 1 and quadrupolar = 2), and hence of fundamental parameters and evolutionary state of the stars. We first use this seismic information to remove from the sample those stars that are in the red clump (RC) phase (i. e. low-mass He-core-burning stars that have likely underwent mass loss earlier in their evolution) and in subsequent phases. Removing these contaminants leaves a sample of 95 red giant branch (RGB) stars whose radial-mode frequencies we have measured to use as the asteroseismic input for inferring robust and precise ages (see Methods and Extended Data Fig. 5  for details). We estimate stellar properties (Supplementary Table.
APOGEE, Gaia and Kepler data We select targets with SDSS-IV/APOGEE spectra and NASA-Kepler light curves by cross matching the APOGEE DR14 catalogue  [4, 46]  with the Kepler Input Catalog (KIC  [47] ). We then cross-match again with the Gaia-DR2 catalogue  [3] , which provides parallax, position and proper motion measurements for the relevant stars. APOGEE provides, in addition of atmospheric parameters (effective temperature and detailed abundances for 23 different chemical elements), highly precise (∼1%) radial velocities for all targets. Radial velocities, combined with Gaia proper motions, are used to derive the orbital parameters. Since we are interested in using asteroseismology to study stellar populations which are likely part of the Milky Way halo, we make a first broad cut to select stars with APOGEE-[Fe/H] < -0.
Seismic data We retrieve Kepler light curves from MAST (https://archive. stsci. edu/kepler/publiclightcurves. html) and measure individual radial-mode frequencies following the approach in ref. [55]. These results were crossmatching with the radial frequency modes using the automatic pipeline PBJam (https://github. com/grd349/PBjam), and with the Kallinger's RG-catalogue  [56]  for the targets in common. Although our main results are based on fitting individual-mode frequencies, we have performed additional tests (see below) working with average seismic indexes  [57, 58]  ∆ν and ν max (mean large frequency separation and frequency at maximum power, respectively). For a fraction of the stars (90 targets) it was also possible to estimate the value of the gravity-mode period spacing  [59]. We measured frequency of at least 3 individual radial modes in 276 targets over 400.
Bayesian stellar parameter inference The stellar parameters of each star in our sample have been estimated by using the open-source code AIMS  [24, 65, 66]  (Asteroseismic Inference on a Massive Scale), that implements a Bayesian inference approach. AIMS evaluates the posterior probability distributions of the stellar parameters using a Markov Chain Monte Carlo (MCMC) ensemble sampler  [67] , and selects stellar models that best fit observation data by interpolating (evolutionary tracks and frequencies) in a pre-computed grid. As demonstrated by several works  [68, 66, 12] , using individual frequencies as observational constrains contributes to significantly reduce the uncertainties affecting estimated global stellar parameters with respect to the precision and accuracy resulting from scaling relations.
The stellar models have been computed using the stellar evolution code CLES  [73] , and following the evolution from the pre-main sequence up to a radius of 25 R on the RGB phase. The details on the adopted physics prescriptions and the computed evolutionary tracks are available at http://doi. org/10. 5281/zenodo. 4032320. The number of models along a track has been chosen to provide a difference of mean large frequency separation between consecutive models of the order of 0. 5%, and the oscillation frequencies of radial modes have been computed for each stellar model using the adiabatic oscillation code LOSC  [74].
Cross-checking using global seismic parameters for a larger set of ex-situ and in-situ stars As an additional test for robustness we use the code PARAM  [86]  to infer masses, radii and ages for the larger set of stars (Extended Data Fig. 5 ). We consider all stars (RC and RGB) with average seismic parameters (∆ν, ν max ) determined by the COR pipeline  [58]. While the results from the detailed analysis are more precise and more accurate (the median age uncertainty given by PARAM is 25% instead 11%), we use the age distribution of the wider sample to check if our main results are compatible with those from an independent modelling code and using average seismic parameters. The grid of models (computed with the code MESA  [87] ) at the base of these calculations includes: a mass loss during RGB evolutionary phase equivalent to a Reimers' parameter η = 0.
We assume the measured ages are drawn from an underlying true age θ distribution that is Gaussian with a mean µ with a standard deviation τ. We assume that the true age distribution is contaminated by stars whose mass is higher than expected (and therefore appear younger), likely due to some poorly understood process such as binary interactions. We model these contaminants as also being drawn from another normal distribution with a mean µ c and spread τ c which has a fractional contribution to the total age distribution (hence the main population contributes 1 -). The peak-bagging was performed using the pipeline PBJam (https://github. com/grd349/PBjam) and the orbital parameter determination with galpy (https://www. galpy. org). The codes CLES and LOSC used for stellar evolution and adiabatic oscillation computations are not publicly available but evolutionary tracks, structure models and oscillation files are available on reasonable request.
Acknowledgements JM, JTM, AM, FV, and EW acknowledge support from the  ERC   Consolidator Grant funding scheme  (project  ASTEROCHRONOMETRY , G. A. n. 772293). FV acknowledges the support of a Fellowship from the  Center for Cosmology and AstroParticle Physics at The Ohio State University. MV is supported by  FEDER -Fundo Europeu de Desenvolvimento Regional  through  COMPETE2020 -Programa Operacional  Competitividade e Internacionalização by these grants:  PTDC/FIS-AST/30389/2017 & POCI-01-0145-FEDER-030389. CC acknowledges partial support from  DFG  Grant  CH1188/2-1  and from the  ChETEC COST Action  ( CA16117 ), supported by  COST (European Cooperation in Science and Technology). G. B. acknowledges fundings from the  SNF   AMBIZIONE grant  No  185805  (Seismic inversions and modelling of transport processes in stars) and from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  833925 , project  STAREX ).
Data Availability All raw observational data are publicly available: Kepler light-curves at https://archive. stsci. edu/kepler/publiclightcurves. html; Gaia-DR2 at https://gea. esac. esa. int/archive and APOGEE Data Release 14 may be accessed via https://www. sdss. org/dr14/. APOGEE-DR14 raw data have been used in top panels of Fig. 2. Processed data such as individual frequencies, orbital and stellar parameters are available in the supplementary material table or on request. Evolutionary tracks are publicly available at http://doi. org/10. 5281/zenodo. 4032320 and theoretical stellar models and oscillation frequencies are available on request.
github. io/aerobulk/, last access: 20 January 2021) package  (Brodeau et al. , 2017)  to compute C D , C θ , and C q in realistic simulations (see Sect. 5). Note that for an ASL forcing strategy u h (z sfc ) and φ(z sfc ) in Eq. (3) would be equal to u LS (z = 10 m) and φ LS (z = 10 m) respectively, while in the ABL coupling strategy those variables are provided prognostically by an ABL1d model. As far as the boundary conditions at z = z top are concerned, Dirichlet boundary conditions  1 ) is a first step before evolving toward a more advanced surrogate model including horizontal advection and fine-scale pressure gradients in the future. A particular focus of the present study is on the appropriate choice of a closure scheme to diagnose the eddy diffusivities K m and K s. This is a key step to properly represent the downward mixing process.
Turbulence closure scheme This subsection describes the turbulence scheme used to compute the eddy diffusivity for momentum and scalars. Those eddy diffusivities are responsible for a vertical mixing of atmospheric variables due to turbulent processes. The turbulence scheme we have implemented in our ABL1d model is very similar to the so-called CBR-1d scheme of  Cuxart et al. (2000) , which is used operationally at Meteo France  (Bazile et al. , 2012). We chose to recode the parameterization from scratch for several reasons: computational efficiency, consistency with the NEMO coding rules, use of a geopotential vertical coordinate, and flexibility to add elements specific to the marine atmospheric boundary layer. CBR-1d is a one-equation turbulence closure model based on a prognostic TKE and a diagnostic computation of appropriate length scales.
Processing of large-scale forcing and Newtonian relaxation As mentioned earlier, the ABL1d model (  1 ) requires threedimensional (x, y, z) large-scale atmospheric variables φ atm LS , while existing uncoupled oceanic forcing strategies require only two-dimensional (x, y) atmospheric variables. This is a difficulty for efficiency reasons since it substantially increases the number of I/Os but also for practical reasons because it requires the development of a dedicated tool to extract large-scale atmospheric data and interpolate them on prescribed geopotential heights from their native vertical grid, which can be either pressure based or arbitrary Lagrangian Eulerian. Such tools have been developed specifically to work with ERA-Interim, ERA5, and operational IFS datasets and are described in Appendix B.
Computational aspects As described in  Maisonnave and Masson (2015) , the NEMO source code is organized to separate the ocean routines on one side and the routines responsible for the surface boundary conditions computation (including sea ice and the coupling interfaces) on the other side. This makes a clear separation between the standard ocean model (OCE component) and the so-called surface module (SAS component). As schematically described in Fig. 3 , the ABL1d model has been implemented within the SAS component, which allows the following useful features: -The ABL1d model can be run in standalone mode (coupled or not with sea ice) with prescribed oceanic surface fields. -The ABL1d model can be run in detached mode; i. e. , the OCE and SAS components run on potentially separate processors and computational grids communicating via the OASIS3-MCT coupling library  (Craig et al.
Sensitivity experiments and objectives To check the relevance of our ABL1d model for idealized atmospheric situations typical of the atmospheric boundary layer over water or sea ice, we performed a set of single-column experiments. Each of those experiments are evaluated with benchmark large eddy simulations (LESs). Moreover, we use standardized test cases from the literature to allow our results to be cross-compared with other well-established ABL schemes. In the following we consider a neutrally stratified (Sect. 4. 2) and a stably stratified (Sect. 4. 3) case as well as a case with a transition from stable to unstable stratification representative of an atmospheric flow over an SST front (Sect. 4. 4). All ABL1d simulations presented here have been performed directly within the SAS component of the NEMO modeling framework and can be reproduced using the code available at https://zenodo.
Three-dimensional atmospheric variables are generated over the 2014-2018 period and vertically interpolated on 50 levels between 10 and 2000 m with a vertical resolution increasing with height. Grid resolution is about 20 m near the air-sea interface and reaches 70 m at the top of the ABL1d domain. The choice of a vertical extent of 2000 m and 50 vertical levels in the ABL1d model is somewhat arbitrary and the robustness of numerical results to these choices will be investigated in future studies. For the simulations presented here, the same horizontal grid and time step ( t = 1200 s) are chosen in the ABL1d and NEMO models. The options associated with the ABL coupling available through the NEMO standard name list are reported in Table  3 , and a detailed profiling of the code is presented in Appendix D in order to assess the overhead associated with the ABL coupling strategy vs.
The current single-column approximation is based on the assumption that the balance is dominated by vertical turbulent mixing, and the effect of other terms is roughly represented by the geostrophic guide and/or the nudging term. The test case presented in Sect. 4. 4 clearly illustrates the limitations of a single-column approach ignoring advective effects. However, before moving to more advanced formulations, our rationale was that the two main bottlenecks in terms of computational cost inherent to the ABL coupling strategy are the reading of 3D atmospheric data and the choice of ABL turbulent scheme. As a first step, we focused on those two aspects to assess whether or not our approach can be a viable option. Even if the justification of our model is not beyond reproach, it already brings an improvement compared to the ASL forcing strategy.
A standalone ABL model of intermediate complexity could also play a role in coupled data assimilation where the current practice is generally to assimilate data separately in the ocean and the atmosphere, ignoring the air-sea interactions, which results in inconsistencies at the air-sea interface in the initial conditions, causing initial shocks in the coupled forecasts (e. g. ,  Mulholland et al. , 2015). We wish to conclude this study by clarifying that the framework we have developed within NEMO is general enough to allow alternative approaches (e. g. , via model-driven empirical models) to be seamlessly tested and confronted with the ABL coupling strategy. defined at cell interfaces between two successive vertical layers is given by p k+ 1 2 = A k+ 1 2 + B k+ 1 2 p s , k ∈ [[1, N ifs ]], where A k+ 1 2 (Pa) and B k+ 1 2 (dimensionless) are constants given by a smooth analytical function defining the vertical grid stretching.
finalize (we get a minus sign in R u LS because the grid in the y direction is flipped in the raw data) (R u LS ) i,j,k = -g wrkY i,j,k -1 2 (dZy i,j +1/2,k + dZy i,j -1/2,k ) , (R v LS ) i,j,k = -g wrkX i,j,k -1 2 (dZx i+1/2,j,k + dZx i-1/2,j,k ). it makes sense to assess the associated overhead. We ran the ASL forced and ABL coupled NEMO simulations for 20 d such that the cost of the initialization step is no longer visible in the averaged cost per time step. Moreover, for the two simulations, the atmospheric data necessary for the computation of the turbulent components of air-sea fluxes are provided every 6 h. We first show in Fig. D1  the elapsed time for each time step over the first 48 h of the simulations with different ways to specify the surface fluxes. For most time steps, the overhead associated with the ABL1d when using the l D80 mixing length is very small (on the order of 4 %); however, every 18 time steps (i.
Nonetheless the small increase in CPU time leaves room for further improvements of the ABL model to relax the horizontal homogeneity assumption. Table  D1. Report of the elapsed time and CPU time in different sections of the NEMO (v4. 0) code for the ASL forcing strategy (left portion of the table) and the ABL coupling strategy (right portion of the table). The timing is averaged on all processors. The right-most column provides a quick description of the task handled by the corresponding section. On top of the timing in seconds the percentage of the total CPU and elapsed time associated with each section is reported in parentheses. The computational overhead associated with the ABL coupling strategy can be estimated from the sbc section and the elapsed and CPU time.
Acknowledgements. We thank  Anton Beljaars  and one anonymous reviewer whose efforts helped to improve earlier versions of this paper. We also thank  Pascal Marquet  and  Sébastien Masson  for useful discussions. Florian Lemarié  and  Jean-Luc Redelsperger  also acknowledge the support by  Mercator-Ocean  and the  Copernicus Marine Environment Monitoring Service (CMEMS)  through contract  22-GLO-HR -Lot 2  (High-resolution ocean, waves, atmosphere interaction). Financial support. This research has been supported by the  H2020 European Institute of Innovation and Technology (IMMERSE  (grant no. 821926 )). Review statement. This paper was edited by  Christina McCluskey  and reviewed by  Anton Beljaars  and one anonymous referee.
Code and data availability. The changes to the NEMO code have been made on the standard NEMO code (release 4. 0). The code can be downloaded from the NEMO website (http://www. nemo-ocean. eu/, last access: 23 June 2020). The NEMO code modified to include the ABL1d model is available in the Zenodo archive (https://doi. org/10. 5281/zenodo. 3904518, Lemarié and Samson, 2020). The name lists and data used to produce the figures are also available in the Zenodo archive.
Upon the publication of the manuscript, the package will be made available at the Comprehensive R Archive Network (CRAN), ready for seamless installation across different computer platforms. Currently, the developer version is available on GitHub (https://github. com/coffeemuggler/sandbox/). The term framework implies that sandbox is not tailored to a specific task but instead provides methods (R functions in our case) to use the tool in different scenarios. Users can reduce or expand the default range of parameters applied to describe the constituents of a sediment section. Due to the design of the R language, new functions can be added with a minimum of formal constraints to easily to exploit the virtual sediment section for user-specific purpose. To make no mistake, sandbox is essentially a one-dimensional model.
40-80 ka,  Zech et al. 2017 ). Rather, it should be considered as two separate layers with a hiatus of some 30 ka. For this site, we can access a detailed granulometric dataset  (Meszner et al. , 2021)  along with a geochronological framework  (Zech et al. , 2017) , both suitable in the context of this study. The grain-size distribution of all 42 samples of the loess section were measured with a Horiba LA950 laser particle sizer, providing 98 grain-size classes. About 0. 5 to 1. 5 mg air-dried and homogenised material have been treated with 10 % HCl for 24 hours and subsequently treated with 40 % H 2 O 2 for 72 hours. Each sample has been measured for 5 s with ultrasonic excitation for 10 s in the device to disaggregate particles mechanically. We used the Mie scattering theory with a refraction index of 1.
EMMAgeo as auxiliary tool To convert the quasi-continuous grain-size distributions into discrete populations, i. e. parametric descriptions (mean and standard deviation) of grain-size rules for sandbox, we unmixed the dataset using the R package EMMAgeo v0. 9. 6  (Dietze and Dietze, 2016, 2019). This package allows end-member modelling analysis (EMMA) of grain-size data sets and uses principles of eigenspace analysis to describe grain-size distributions as a linear combination of end-member loadings and scores. loading to each sample. Thus, scores can be considered as a description of the relevance of a transport process for a given sample. In the context of this study, loadings refer to the grain-size distribution of particular populations (parameter definition), and scores refer to the depth-dependent likelihood of a population to be sampled (rule definition).