dataesr/scientific-paragraphs-categorization huggingface dataset:
The Millennial V2 model may capture the observed variation in C stocks across a gradient of NPP better than the Century model (Fig. S10 ). This may be due to the difference in the way NPP and C stock are represented in the two models. In the Century model, there is a constrained linear relationship between NPP and C stock (Fig. S4c ), whereas Millennial V2 has a less linear relationship (Fig. 3c , Fig. S3c ). The model also provides a reasonable distribution of turnover times compared to a global distribution of turnover times measured using SOC/respiration rate for the top 20 cm under an assumption of steady state (Fig. 9 ). Though the represented depth varies for different sites in our dataset, the median depth represented in the dataset was 20 cm (6 cm-62 cm). The median turnover time of Millennial V2 (17.
2 years) is higher than that of  Chen et al. (2013)  (11. 2 years), but it is much more similar to the observations than the median turnover time of the Century model (37. 0 years). Finally, we used GBM models described in Section 2. 5 trained on each of the three datasets, to quantify the performance of a purely empirical model in contrast to process-based models like Millennial V2 and Century, as well as the extent to which the environmental forcings (e. g. , NPP, soil temperature, pH) are related to the SOC stock. We found that the GBM model was able to predict C stocks better than the processbased models. Similar to Millennial V2 and Century, the GBM model was most able to predict C stocks for the sites in the VR dataset, followed closely by those in the KG dataset, and finally those in the LUCAS dataset (Table  5 , Fig.
Millennial V2 and Century model performance Millennial V2 simulates spatial variation in soil C stocks, including measurable soil fractions, better than the widely-used Century model, based on several model performance metrics, including RMSE, AIC, MAE, MBE and R 2 (Table  3 ). Millennial V2 also predicts the C contained within the MAOM pool, which generally contains older organic material than other fractions, especially at depth  (Conen et al. , 2008; Hicks Pries, 2017; Poeplau et al. , 2018). MAOM was generally observed to be the largest soil C pool across all dataset sites. Because Millennial V2 more explicitly simulates processes relating C pools to mineral capacity, pH, temperature and soil moisture, it better predicts not only the mean C stock across different sites (Fig. 4 ), but also better predicts the distribution of C stocks across latitudes (Fig.
Temperate coniferous forests (N = 30), deserts (N = 63), boreal forest (N = 111), and tropicalmoist broadleaved forests (N = 171) are also underrepresented in certain datasets (Fig. S8 ). Therefore, mismatches in distributions of observed and predicted C stocks could be attributed to sample size effects, understanding that C stocks have a high spatial heterogeneity within biomes. The choice of dataset affects the accuracy of both Millennial V2 and Century, not only due to sample size effects but due to the source and quality of ancillary data used. We used a GBM model to empirically quantify the relationship between the environmental factors used as model forcing (i. e. , soil temperature, clay and silt content, NPP, soil moisture, pH) and C stocks (Table  5 ). Using this approach, it is clear that there is less of a relationship between environmental factors at LUCAS sites and the measured soil C stock, with only 15% of cross-validated variance explained, compared to over 60% for the other two datasets.
The LUCAS sites also rank the influence of environmental factors on C stock differently than the other datasets, with pH being by far the most influential factor at LUCAS sites, compared to soil temperature at other sites. This may explain why Millennial V2 only explains 4% of variation in C stocks at LUCAS sites (compared to 31% and 46% for the other two datasets), and why a different microbial-mineral model  (Robertson et al. , 2019)  fit to LUCAS site data has a similar coefficient of determination, although models are generally able to capture mean C stocks across biome  (Figs. 7, 8)  and land type (Fig. S13 ) classifications. It is not immediately clear what may cause greater heterogeneity in the LUCAS dataset, but the fact that SOC stocks are more dependent on pH than soil temperature suggests a potential role of land use history and past management practices.
Past and current land management may be a source of uncertainty across all the datasets, as well as unmet assumptions of steady state. For example, the KG dataset contains 284 cropland sites, which are likely to have changing C stocks. When we repeat our analysis excluding these sites (Table  S2 , Table  S3 ), some model performance metrics improved while others worsened (Table  S4 , Table  S5 ). In general, the coefficients of determination improved while metrics based on error worsened. Although we did not see clear evidence for model improvement by excluding these sites, the difference in model performance supports the idea that land use history and management may have important effects on the spatial variation in C stocks that cannot be captured by models that do not represent these processes.
Comparing process-based models to empirical models The GBM model predicts steady-state C stocks with higher accuracy Fig. 9. Density plots showing the distribution of turnover times in  Chen et al. (2013) (denoted Observed; median [1st, 3rd quartiles] = 11. 2 [7. 4, 17. 5] ), the Century model  (37. 0 [31. 6, 45. 7] ), and the Millennial V2 model  (17. 2 [11. 6, 23. 5] ). than both the Millennial V2 and Century models, raising the question, why not replace process-based models entirely with empirical models? One reason is that a process-based model can be more generalizable. For example, when we used parameters that were fit to one dataset to predict stocks from a different dataset (Table  S6 ), Millennial V2 was able to explain 3-46% of variation in the other two datasets. Conversely, the GBM model could only explain 0-22% of variation in the other datasets (Table  S6 ).
Therefore, Millennial V2 predictions made by model parameters that have been fit to a subset of global-scale data are likely more predictive than those made by an empirical model fit to the same data. Because process-based models are more generalizable, they may be more reliable under novel conditions, whereas empirical models are most useful for interpolation. The same principle can explain why a more process-rich model like Millennial V2 appears to outperform a more empirical model such as Century (Fig. 4 ). Another benefit of process-based models is the ability to predict quantities other than the one that has been fitted. For example, by fitting the model to C stocks, we also predicted reasonable turnover times, which are related to both the stock size and the emission (respiration) rate.
Future data and model needs The large variation in model performance depending on the sample size or the data source underscores the need for abundant and standardized data. Across these datasets, MAOM was measured using three different particle size thresholds (<50, 53, and 60 μm), and POM and aggregates were not separated in the larger particle size fraction. Measurements of small C pools that are sensitive to environmental factors, such as microbial biomass and low molecular weight C, were largely not available. Although microbial biomass is generally less than 5% of total SOC  (Xu et al. , 2013) , it is the primary driver of C emissions from the soil, and small changes to this pool may have large effects on total SOC and C emissions. Recent work has also shown that DOC has a large potential to contribute to C storage, especially in the mineral fraction  (Abramoff et al.
, 2021). Therefore, large, standardized datasets of Millennial V2 pools (Fig. 1 ) and fluxes (e. g. , respiration rate, enzyme activity) will allow for better global-scale parameterization of the model. Although Millennial V2 is more complex than many soil models, it has structural limitations that would benefit from future work, such as consideration of nitrogen (N) and phosphorus (P) limitations to C storage  (Davies et al. , 2020; Spohn, 2020) , and depth-dependence of microbial activity and substrate availability  (Dove et al. , 2021). We further acknowledge that this study evaluates the spatial variation in C stock only, and that the temporal dynamics of Millennial V2 require further testing. Several long-term experiments exist across the world  (Richter et al. , 2007) , but most have not made measurements of soil fractions at different time periods.
Future measurement campaigns, such the Joint Research Center's next planned sampling campaign for the LUCAS sites, will add much needed temporal resolution to the existing dataset. Recent and developing spectroscopy-based methods may also allow for low-cost estimates of soil fractions from ongoing experiments as well as archived soils  (Baldock et al. , 2013; Ramírez et al. , 2021; Sanderman et al. , 2021) , creating datasets of repeat measurements for model evaluation. C models are constantly evolving to represent the most updated knowledge of the soil system. We now seek to associate that knowledge more directly with measurable quantities, both to make models easier to constrain and also to ensure that model predictions are accurate for the right reasons, rather than the result of compensating biases.
Code and data availability The code for the models tested here are available on the github repository: https://github. com/rabramoff/Millennial. Millennial V1 and V2 are available in both R and Fortran. We have also included the scripts used to produce the figures and analyses presented in this manuscript as R markdown notebooks within the same repository: https://github. com/rabramoff/Millennial/tree/master/R/analysis. Some small data files are also included in the same folder. Other datasets are described in the manuscript with citations and when available their URLs are also provided in the analysis code.
Model fit of SOC to measured values. R 2 in = coefficient of determination of the training dataset (i. e. , in-sample), R 2 out = coefficient of determination of the test dataset (i. e. , out-of-sample), RMSE = root mean square error, MAE = mean absolute error, MBE = mean bias error. AIC = Akaike Information Criterion. N is the number of SOC measurements included in each dataset.
Millennial V2 model fit of MAOM and non-MAOM (POM + AGG + LMWC + MIC) pools to measured values. R 2 in = coefficient of determination of the training dataset (i. e. , in-sample), R 2 out = coefficient of determination of the test dataset (i. e. , out-of-sample), RMSE = root mean square error, MAE = mean absolute error, MBE = mean bias error. NS = not significant at the P < 0. 05 level. N is the number of SOC measurements included in each dataset.
Performance metrics of the gradient-boosted machine learning algorithm prediction of soil C stock and the relative influence of different model forcings for the different datasets. R 2 in = coefficient of determination of the training dataset (i. e. , in-sample), R 2 out = coefficient of determination of the test dataset (i. e. , out-of-sample), RMSE = root mean square error, MAE = mean absolute error, Soil Temp = soil temperature, NPP = net primary production.
Acknowledgements R. Z. A. thanks  Jinyun Tang  and  Elisa Thébault  for helpful discussion about model formulation. R. Z. A. was supported by the  French government  grant " Make Our Planet Great Again " and by a  Marie Skłodowska-Curie Individual Fellowship  (Grant no. 834-169 ) from the  European Union 's  Horizon 2020 program. K. G. was supported as a Lawrence Fellow at  Lawrence Livermore National Lab (LLNL)  by the  LLNL-LDRD Program  under Project No. 21-ERD-045  and the  US DOE Office of Science, Office of Biological and Environmental Research ,  Genomic Science Program  as part of the  LLNL Microbes Persist Scientific Focus Area ,  SCW1632. Work at LLNL was conducted under the auspices of US  DOE  Contract  DE-AC52-07NA27344. X. X. has been supported by the  ORNL Terrestrial Ecosystem Science Scientific Focus Area  ( ORNL TES-SFA ) and  NGEE Arctic projects  and  DE-SC0014416 , which are supported by the  Office of Biological and Environmental Research in the Department of Energy Office of Science.
We present a new gridded sea surface height and current dataset produced by combining observations from nadir altimeters and drifting buoys. This product is based on a multiscale and multivariate mapping approach that offers the possibility to improve the physical content of gridded products by combining the data from various platforms and resolving a broader spectrum of ocean surface dynamic than in the current operational mapping system. The dataset covers the entire global ocean and spans from 1 July 2016 to 30 June 2020. The multiscale approach decomposes the observed signal into different physical contributions. In the present study, we simultaneously estimate the mesoscale ocean circulations as well as part of the equatorial wave dynamics (e. g. tropical instability and Poincaré waves).
Introduction Several oceanographic applications (e. g. operational oceanography, marine weather, and climate monitoring) rely on high-quality observational datasets. The European Union (EU) Copernicus Marine and Climate Change Services provide operational services and indicators on the observed state of the climate. Sea-level and surface currents are, among others, key variables distributed by the services. They are also listed as Essential Climate Variables (ECVs) for the detection of climate change and the characterization of climate system variability  (Bojinski et al. , 2014). As part of the Copernicus Services, the Sea Level Thematic Assembly Centre (SL-TAC) delivers near-real-time and delayed-time sea-level and surface current products (along-track Level-3 and gridded Level-4 products) that are used by the ocean science community to study, un-Published by Copernicus Publications.
Sea-level anomaly products The global ocean sea surface height (SSH) observations are from the (delayed time, DT) Level-3 altimeter satellite along-track data, reprocessed in 2021 and distributed by the EU Copernicus Marine Service (product reference SEALEVEL_GLO_PHY_L3_MY_008_062, https://doi. org/ 10. 48670/moi-00146,  Pujol et al. , 2022a). These data cover the period from 1 January 1993 to 31 December 2020 over the world ocean (excluding ice-covered areas; see, for example, Fig. 1 ) and are available at a sampling rate of 1 Hz (∼ 7 km spatial spacing). Homogenization and crossvalidation are applied to the dataset to remove any residual orbit error, long-wavelength error (lwe), large-scale biases, and discrepancies between different data streams. The list of geophysical and environmental corrections applied to the datasets is described in the quality information document  (Taburet et al.
Experiments We produced 4 years (from 1 July 2016 to 30 June 2020) of SSH maps using the MIOST multiscale and multivariate approach by combining the Level-3 altimeter dataset from SARAL/AltiKa, Envisat, Jason-1, Jason-2, Jason-3, Cryosat-2, Haiyang-2A, Haiyang-2B, Sentinel-3A, and Sentinel-3B missions, the Level-3 Arctic lead sea-level anomaly products from SARAL/AltiKa, Sentinel-3A, and CryoSat-2 missions, and geostrophic current anomaly data from the AOML drifter database. These MIOST products are available on the AVISO + (Archivage, Validation et Interprétation des données des Satellites Océanographiques) website (see Sect. 5, "Data availability", for more details). Specific maps were also made to quantitatively assess the quality of these MIOST products. list of experiments conducted in this study, indicating the input data used in the mapping and the physical content of the maps.
DUACS allsat-1 and MIOST allsat-1 experiments focus on the geostrophic variability. These SSH maps were produced from six altimeters (Jason-3, Cryosat-2, Sentinel-3A, Sentinel-3B, Haiyang-2A, Haiyang-2B) for the period 1 January 2019 to 31 December 2019, excluding one altimeter (Saral/AltiKa, over open-ocean region) from the mapping to perform independent assessments. The MIOST allsat-1 80 % drifters + equatorial waves + L3 Arctic experiment focuses on the geostrophic and equatorial wave variabilities. This experiment is based on (1) 80 % of the drifter data, (2) the six altimeters previously mentioned over ocean, and (3) lead altimeter observations. The Saral/AltiKa dataset (over openocean region) and the remaining 20 % of the drifter trajectories were here excluded from the mapping to perform independent assessments.
For a more descriptive assessment by wavelength and to avoid spatio-temporal filtering of independent and study datasets, diagnostics can be performed in frequency space, using spectral analysis of SSH altimetry and gridded datasets. More specifically, a spectral analysis can be applied to altimetry data to estimate the effective resolution of gridded SSH products. It is described, for example, in  Ballarotta et al. (2019). Here, we recall the main processing steps for the estimation of the effective resolution: (1) the SSH map data are interpolated to the locations of independent SSH along-track data, (2) the along-track and interpolated data are divided into overlapping segments of 1500 km length every 300 km, (3) each segment is stored in a database and referenced by its median coordinates (longitude, latitude), and (4) finally, between latitudes 90 • N-90 • S and longitudes 0 • -360 • E, we consider 10 • × 10 • longitude × latitude boxes for the global products every 1 • increment.
Data availability The MIOST gridded products are hosted on the AVISO+ (Archivage, Validation et Interprétation des données des Satellites Océanographiques) website (https://doi. org/10. 24400/527896/a01-2022. 009, Ballarotta et al. , 2022). The reference DUACS maps are hosted on the EU Copernicus Marine Service portal (https://doi. org/10. 48670/ moi-00148,  Pujol et al. , 2022b). The multiscale and multivariate products are distributed on a regular grid: the spatial grid extends from 0 to 360 • E in longitude and 80 • S to 90 • N in latitude, with a grid spacing of 0. 1 • ; the temporal grid covers the period 1 July 2016 to 30 June 2020 with a time step of 1 d. The dataset is distributed in netCDF4 format. Each netCDF file contains six variables: sla, adt, ugosa,vgosa, ugos, and vgos (see the list of variables available in the MIOST product in Fig.
69 ± 1. 62 years; 8 females, 8 males). See experimental design for more detailed information about the groups. We recorded polysomnography (PSG) during full night sleep for all participants except adults in the wake-first group. Therefore, comparison of electrophysiological data between adults and adolescents was based on the adult sleep-first group and both adolescent groups. To ensure similar juggling learning experience, we only included adults and adolescents in the sleep-first group when analyzing the relationship between electrophysiological measures and behavioral performance. All participants and the legal custodians of the adolescents provided written informed consent before participating in the study. The study protocol was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the University of Salzburg (EK-GZ:16/2014).
ACKNOWLEDGMENTS This research was supported by  Austrian Science Fund  ( P25000-B24 ) and the  Centre for Cognitive Neuroscience Salzburg (CCNS). M. A. H. was additionally supported by the  Doctoral Collage 'Imaging the Mind'  ( FWF ,  Austrian Science Fund   W1233-G17 ). R. F. H. is supported by the  German Research Foundation  ( DFG ,  HE 8329/2-1 ), the  Hertie Foundation (Hertie Network of Excellence in Clinical Neurosciences)  and the  Jung Foundation for Science and Research  ( Ernst Jung Career Advancement Award ).
Data analyses We used functions from the Fieldtrip toolbox  (Oostenveld et al. , 2011) , EEGlab toolbox  (Delorme & Makeig, 2004) , CircStat toolbox  (Berens, 2009)  and custom written code implemented in MatLab 2015a (Mathworks Inc. ) for data analyses. Irregular autospectral analysis (IRASA  (Wen & Liu, 2016 )) was conducted using code obtained from the original research paper.
DATA AVAILABILITY The behavioral and electrophysiological preprocessed data and scripts to replicate the main conclusions and figures of the paper are available at https://datadryad. org/stash/share/177ueSz3dyTr3-x6pRaUbZncoOZXNndr_SThSNNkx0A (doi:10. 5061/dryad. qfttdz0gh).
Renewables EROI We use the global final EROI curves generated for wind and solar energies by  Dupont et al. (2018 Dupont et al. ( , 2020)). 9 The curves describe how the mean EROI of renewable energy production facilities (onshore wind turbines, offshore wind turbines or solar panels) evolves as new facilities are added to scale up renewable production worldwide. These curves were built using a grid-cell approach: a mesh of 115k cells was applied on the Earth's surface and a series of databases were crossed in order to obtain for each cell the mean solar irradiation, the mean wind speed at an altitude of 100 m, the current land use, etc. Based on these data, the maximum wind and solar energy production potential was computed for each cell, as well as the energy inputs required for building, maintaining, and decommissioning the wind turbines and solar panels.
Global energy intensity of the economy Over the last three decades, the energy intensity of GDP has been decreasing for almost all world regions  (Ahmad and Zhang, 2020; Vita et al. , 2021). Even though thermodynamic limits ultimately constrain energy efficiency, significant gains are still possible  (Cullen et al. , 2011). Quantifying these gains is a complex and intricate task, subject to multiple uncertainties  (de Blas et al. , 2019). We here rely on EXIOBASE3  (Stadler et al. , 2018) , a global multiregional input-output database (GMRIO) to calculate energy footprints of GDP for the period 2011-2017 and project their future evolution. EXIOBASE3 provides economic data in current euros and must be corrected for purchasing power parity (PPP), using sector-level PPPs in constant currencies from the International Comparison Panel (ICP) cycles  2011 and 2017 (World Bank, 2020)  and Eurostat to distinguish between the dynamics of goods and services  (Andrieu et al.
The energy intensity of the capital stock of the final goods sector is supposed to be that of Gross Fixed Capital Formation (GFCF) from EXIOBASE, corrected for purchasing power parity using ICP data and capitaldetails from Penn World Tables (PWT) (University of Groningen, 2021). Finally, the energy intensity of the production of energy capital stock is supposed to be equal to that of the Machinery and Equipment sector of EXIOBASE, also corrected for purchasing power parity using ICP and PWT data. This distinction is important since the energy intensity of Machinery and Equipment for the year 2017 is 81% above that of GFCF. However, the yearly improvement of the energy intensity of Machinery and Equipment is almost three times higher than that of GFCF (1. 7 vs. 0. 59% per year). Fig. 2  summarizes the computed values of the different energy intensities for the year 2019 and their projected evolution until 2060, which corresponds to the end time of our simulations (see Section 4).
6 equal zero and all the equations defining the intermediate variables are met. On top of that, we impose minimum and maximum values for each variable and parameter. The values of these lower and upper bounds are either drawn from our knowledge of economics or inspired from several data sources. The main data sources used are the Penn World Table  10. 0, the IEA data tables,  Dupont et al. (2021a)  and the Bank for International Settlements' database. 28 For processing the data from the IEA and making them compatible with our model, we used the approach described in section 6. 1. 2 of  Dupont (2021). 𝜋 𝑏,𝑢 = 𝛽 𝑏 ( 𝜇 𝑏 (𝑑 𝑓 +  𝑒 𝑑 𝑒 ) -𝑜𝑓 𝑏 ) 𝑢𝑐 𝑓 = 𝜔 𝑓 + 𝜖 𝑓  𝑒 + 𝛾 𝑓 𝛿 𝑓 𝜋 𝑓 = 1 𝛾 𝑓 ( 1 -𝑢𝑐 𝑓 ) -𝑟 𝑑 𝑓 𝜅 𝑤,𝑓 = 𝜔 𝑓 ( 1 -𝑠 𝐹 ,𝑤 )  𝑒 𝜖 cons 𝑓 ( 𝑒 ) + 1 +  𝑤,𝑓 ( 1 𝛾 𝑓 + 𝛼 𝑓 𝛼 𝑒  𝑒 ) The calibration is performed by solving a non-linear optimization problem.