dataesr/scientific-paragraphs-categorization huggingface dataset:
43. Follett KA, Weaver FM, Stern M, et al. Pallidal versus subthalamic deep-brain stimulation for Parkinson's disease. N Engl J Med 2010; 362(22):2077-2091. https://doi. org/10. 1056/NEJMoa0907083 44. Rusz J, Tykalova T, Ramig LO, Tripoliti E. Guidelines for speech recording and acoustic analyses in Dysarthrias of movement disorders. Mov Disord 2021;36(4):803-814. https://doi. org/10. 1002/mds. 28465 Supporting Data Additional Supporting Information may be found in the online version of this article at the publisher's web-site. Downloaded from https://movementdisorders. onlinelibrary. wiley. com/doi/10. 1002/mds. 29282 by Readcube (Labtiva Inc. ), Wiley Online Library on [20/12/2022]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License E F F E C T S O F E A R L Y S T I M O N S P E E C H 15318257, 0, Movement Disorders, 2022 11.
Historical period 1850-2021 The reported estimate of the global ocean anthropogenic CO 2 sink S OCEAN is derived as the average of two estimates. The first estimate is derived as the mean over an ensemble of 10 global ocean biogeochemistry models (GOBMs, Tables  4  and A2 ). The second estimate is obtained as the mean over 4822 P. Friedlingstein et al. : Global Carbon Budget 2022  Table  4. References for the process models, bookkeeping models, ocean data products, and atmospheric inversions. All models and products are updated with new data to the end of year 2021, and the atmospheric forcing for the DGVMs has been updated as described in Appendix C2. 2. New this year. a See also  Asaadi et al. (2018). b See also  Tian et al. (2011). c The dynamic carbon allocation scheme was presented by  Xia et al.
The GOBMs simulate both the natural and anthropogenic CO 2 cycles in the ocean. They constrain the anthropogenic air-sea CO 2 flux (the dominant component of S OCEAN ) by the transport of carbon into the ocean interior, which is also the controlling factor of present-day ocean carbon uptake in the real world. They cover the full globe and all seasons and were recently evaluated against surface ocean carbon observations, suggesting they are suitable to estimate the annual ocean carbon sink  (Hauck et al. , 2020). The data products are tightly linked to observations of f CO 2 (fugacity of CO 2 , which equals pCO 2 corrected for the non-ideal behaviour of the gas;  Pfeil et al. , 2013) , which carry imprints of temporal and spatial variability, but are also sensitive to uncertainties in gas exchange parameterizations and data spar-sity.
A fourth simulation (sim D; historical climate forcing and constant atmospheric CO 2 ) is used to compare the change in anthropogenic carbon inventory in the interior ocean (sim A minus sim D) to the observational estimate of  Gruber et al. (2019)  with the same flux components (steady state and non-steady state anthropogenic carbon flux). Data products are adjusted to represent the full ice-free ocean area by a simple scaling approach when coverage is below 99 %. GOBMs and data products fall within the observational constraints over the 1990s (2. 2 ± 0. 7 GtC yr -1 ,  Ciais et al. , 2013)  after applying adjustments. S OCEAN is calculated as the average of the GOBM ensemble mean and data product ensemble mean from 1990 onwards. Prior to 1990, it is calculated as the GOBM ensemble mean plus half of the offset between GOBMs and data product ensemble means over  1990-2001.
The 2022 projection The ocean CO 2 sink forecast for the year 2022 is based on the annual historical and estimated 2022 atmospheric CO 2 concentration  (Dlugokencky and Tans, 2022) , the historical and estimated 2022 annual global fossil fuel emissions from this year's carbon budget, and the spring (March, April, May) Oceanic Niño Index (ONI)  (NCEP, 2022). Using a non-linear regression approach, i. e. a feed-forward neural network, atmospheric CO 2 , ONI, and fossil fuel emissions are used as training data to best match the annual ocean CO 2 sink (i. e. combined S OCEAN estimate from GOBMs and data products) from 1959 through 2021 from this year's carbon budget. Us-ing this relationship, the 2022 S OCEAN can then be estimated from the projected 2021 input data using the non-linear relationship established during the network training.
The 2022 projection Like for the ocean forecast, the land CO 2 sink (S LAND ) forecast is based on the annual historical and estimated 2022 atmospheric CO 2 concentration  (Dlugokencky and Tans, 2021) , historical and estimated 2022 annual global fossil fuel emissions from this year's carbon budget, and the summer (June, July, August) ONI  (NCEP, 2022). All training data are again used to best match S LAND from 1959 through 2021 from this year's carbon budget using a feed-forward neural network. To avoid overfitting, the neural network was trained with a variable number of hidden neurons (varying between 2-15), larger than for S OCEAN prediction due to the stronger land carbon interannual variability. As done for S OCEAN , a pre-training selects the optimal number of hidden neurons based on 20 % withheld input data, and in a second step, an ensemble of 10 forecasts is produced to provide the mean forecast plus uncertainty.
, 2019; Schuh et al. , 2019). Four inversion systems (CAMS-FT21r2, CMS-flux, GONGGA, THU) used satellite xCO 2 retrievals from GOSAT and/or OCO-2, scaled to the WMO 2019 calibration scale. One inversion this year (CMS-Flux) used these xCO 2 data sets in addition to the in situ observational CO 2 mole fraction records. The original products delivered by the inverse modellers were modified to facilitate the comparison to the other elements of the budget, specifically on two accounts: (1) global total fossil fuel emissions, including cement carbonation CO 2 uptake, and (2) riverine CO 2 transport. Details are given below. We note that with these adjustments the inverse results no longer represent the net atmosphere-surface exchange over land and ocean areas as sensed by atmospheric observations.
1) are adjusted in the latitudinal partitioning we present to ensure agreement with the estimate of E FOS in this budget. We also note that the ocean fluxes used as prior by six out of the nine inversions are part of the suite of the ocean process models or f CO 2 data products listed in Sect. 2. 4. Although these fluxes are further adjusted by the atmospheric inversions, it makes the inversion estimates of the ocean fluxes not completely independent of S OCEAN assessed here. To facilitate comparisons to the independent S OCEAN and S LAND , we used the same corrections for transport and outgassing of carbon transported from land to ocean, as has been done for the observation-based estimates of S OCEAN (see Appendix C3). The atmospheric inversions are evaluated using vertical profiles of atmospheric CO 2 concentrations (Fig.
, 2018; O'Rourke et al. , 2021) , and PRIMAP-hist version 2. 3. 1  (Gütschow et al. , 2016 (Gütschow et al. , , 2021)) , although these data sets are not entirely independent of each other  (Andrew, 2020a). CDIAC-FF has the lowest cumulative emissions over 1750-2018 at 437 GtC, GCP has 443 GtC, CEDS 445 GtC, PRIMAP-hist TP 453 GtC, and PRIMAP-hist CR 455 GtC. CDIAC-FF excludes emissions from lime production, while neither CDIAC-FF nor GCP explicitly include emissions from international bunker fuels prior to 1950. CEDS has higher emissions from international shipping in recent years, while PRIMAP-hist has higher fugitive emissions than the other data sets. However, in general these four data sets are in relative agreement as to total historical global emissions of fossil CO 2.
Final year 2021 Global fossil CO 2 emissions were 5. 1 % higher in 2021 than in 2020 because of the global rebound from the worst of the COVID-19 pandemic, with an increase of 0. 5 GtC to reach 9. 9 ± 0. 5 GtC (including the cement carbonation sink) in 2021 (Fig. 5 ), distributed among coal (41 %), oil (32 %), natural gas (22 %), cement (5 %), and others (1 %). Compared to the previous year, 2021 emissions from coal, oil, and gas increased by 5. 7 %, 5. 8 %, and 4. 8 %, respectively, while emissions from cement increased by 2. 1 %. All growth rates presented are adjusted for the leap year unless stated otherwise. In 2021, the largest absolute contributions to global fossil CO 2 emissions were from China (31 %), the USA (14 %), The partitioning is based on nearly independent estimates from observations (for G ATM ) and from process model ensembles constrained by data (for S OCEAN and S LAND ) and does not exactly add up to the sum of the emissions, resulting in a budget imbalance (BI M ), which is represented by the difference between the bottom red line (mirroring total emissions) and the sum of carbon fluxes in the ocean, land, and atmosphere reservoirs.
all forest areas  (Schwingshackl et al. , 2022). Comparing E LUC and NGHGI on the basis of the four component fluxes  (Grassi et al. , 2022b) , we find that NGHGI deforestation emissions are reported to be smaller than the bookkeeping estimate (1. 1 GtC yr -1 averaged over 2012-2021). A reason for this lies in the fact that country reports do not (fully) capture the carbon flux consequences of shifting cultivation. Conversely, carbon uptake in forests (afforestation, reafforestation, and forestry) is substantially larger than the bookkeeping estimate (1. 75 GtC yr -1 averaged over 2012-2021), owing to the inclusion of natural CO 2 fluxes on managed land in the NGHGI. Emissions from organic soils and the net flux from other transitions are similar to the estimates based on the bookkeeping approach and the external peat drainage and burning data sets.
S11 ) selected for their climatological homogeneity  54. The S2M reanalysis uses a 300 m vertical resolution  28. In our study, for each massif, meteorological data depends only on elevation (one data point every 300 m) considering a flat surface. BC and dust deposition from CNRM-ALADIN63. BC and dust deposition fluxes are obtained from the regional climate model CNRM-ALADIN63  29. This model includes an interactive tropospheric aerosol scheme able to represent the main aerosol species such as BC and dust in the troposphere. These aerosols are prognostic variables, subject to transport, dry deposition and in-cloud and below-cloud scavenging. In this study, hourly output of dry and wet BC and dust deposition fluxes were used, coming from a simulation over the 1979-2018 period driven by the ERA-Interim reanalysis in order to ensure a realistic timeline of the evolution of aerosol deposition.
). (3) On the trend in snow cover The trends in meteorological forcing (temperature and solid precipitation), BC and dust, are computed over the 1979-2018 period, and a Student's test (t test with a 0. 05 confidence interval) is applied to evaluate the significance. The trends in simulated snow cover (SMOD) and CMD of S pure and S BC+Dust are quantified following the same method (Fig. 3 ). Then, for each year, the SMOD differences between S pure and S BC+Dust (and also S BC and S Dust separately) is computed, in order to obtain a dataset (39 years) from these differences. The trend of this dataset is then computed and its significance is evaluated following the t-test with a 0. 05 confidence interval. This method allows to evaluate the significance of the impact of the BC and dust on the trend.
Acknowledgements The authors thank  Florent Dominé ,  Matthieu Vernay  and  Delphine Six  for helpful conversations. We also thank the scientific editor  Kasey Bolle  sand two anonymous reviewers for their comments and suggestions, which significantly improved the quality of the manuscript. CNRM/CEN and IGE are part of  Labex OSUG@2020  (investissement d'avenir- ANR10 LABX56 ). M. D. has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 949516 , IVORI). This study was supported by the  ANR  programme  ANR-16-CE01-0006  EBONI; and the  CNES   APR grant MIOSOTIS. This research was at least partially supported by  Lautaret Garden-UMS 3370  ( Univ. Grenoble Alpes, CNRS, SAJF, 38000 Grenoble, France ), member of  AnaEE-France  ( ANR-11-INBS-0001AnaEE -Services, Investissements d'Avenir frame) and of the eLTER-Europe network (Univ.
Grenoble Alpes, CNRS ,  LTSER Zone Atelier Alpes, 38000 Grenoble, France). As part of the project CDP TRAJECTORIES, M. M. has been funded by the  French National Research Agency  in the framework of the " Investissements d'avenir" programme  ( ANR-15-IDEX-02 ). P. G. received support from the  NASA Understanding Changes in High Mountain Asia  ( NNH19ZDA001N-HMA ) and the  Earth Surface Mineral Dust Source Investigation (EMIT) , a  NASA Earth Ventures-Instrument  ( EVI-4 ) Mission.
Code availability TARTES model is available here: http://snowtartes. pythonanywhere. com/ (web application and Python module download). The Crocus snowpack model (including all physical options of the ESCROC system) is developed inside the opensource SURFEX project. The SURFEX version used in this work is the same as in Cluzet et al. 73  (git tag Cro-cO_v1. 0; source code available at https://doi. org/10. 5281/zenodo. 3774861, see this reference for more detailed technical information about code access).
The multidisciplinary project Prediction of Air Pollution in Latin America and the Caribbean (PAPILA) is dedicated to the development and implementation of an air quality analysis and forecasting system to assess pollution impacts on human health and economy. In this context, a comprehensive emission inventory for South America was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4. 1 (developed by joining CEDS trends and EDGARv4. 3. 2 historical data), enriching it with derived data from locally available emission inventories for Argentina, Chile and Colombia. This work presents the results of the first joint effort of South American researchers and European colleagues to generate regional maps of emissions, together with a methodological approach to continue incorporating information into future versions of the dataset.
This version of the PAPILA dataset includes CO, NO x , NMVOCs, NH 3 and SO 2 annual emissions from anthropogenic sources for the period 2014-2016, with a spatial resolution of 0. 1°x 0. 1°over a domain that covers 32°-120°W and 34°N-58°S. PAPILA dataset is presented as netCDF4 files and is available in an open access data repository under a CC-BY 4 license: http://dx. doi. org/10. 17632/btf2mz4fhf. 2. A comparative assessment of PAPILA-CAMS datasets was carried out for (i) the South American region, (ii) the countries with local data (Argentina, Colombia and Chile), and (iii) downscaled emission maps for urban domains with different environmental and anthropogenic factors. Relevant differences were obtained both at country and urban level for all the compounds analysed. Among them, we found that when comparing total emissions of PAPILA versus CAMS datasets at the national level, higher levels of NO x and considerably lower of the other species were obtained for Argentina, higher levels of SO 2 and lower of CO and NO x for Colombia, and considerably higher levels CO, NMVOCs 1.
These regional particularities have direct consequences not only on the level and chemical profiles of the pollutants discharged to the atmosphere, but also on the specific locations where these emissions occur and on the population exposed to their environmental and health effects. Assessing the impact of atmospheric emissions as well as designing mitigation strategies, require reliable atmospheric emissions inventories (AEIs), which include spatially disaggregated emissions covering the entire region of interest in a transparent and consistent way in terms of emission sources and estimation methodologies  (Kuenen et al. , 2014). There is a wide range of global AEIs covering SA for different species and periods that meet the mentioned requirements. Some of the AEIs worth mentioning include: the Emissions Database for Global Atmospheric Research (EDGAR)  (Janssens-Maenhout et al.
, 2019; EDGAR, 2021) , the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants (ECLIPSE)  (Stohl et al. , 2015) , the Community Emissions Data System (CEDS)  (Hoesly et al. , 2018) , the integrated assessment model Greenhouse gas -Air pollution Interactions and Synergies (GAINS)  (Klimont et al. , 2017) , or the Copernicus Atmosphere Monitoring Service datasets (CAMS)  (Granier et al. , 2019). Across the region, government efforts on AEIs are mainly focused on GHGs in line with the international commitments under the United Nations Convention on Climate Change (UNFCCC). The regional community of GHG inventory compilers has grown remarkably in the last two decades and in many cases has helped to improve the collection of activity data including specific areas of national statistics systems.
, 2020a). From this, it is plausible to assume that better emission estimates would be obtained by enriching the comprehensive global AEIs with locally generated information. This work presents what to our knowledge constitutes the first AEIs from anthropogenic sources covering the entire SA region, which combines in a proper and rigorous way local available information with a global database. For this purpose, the dataset CAMS-GLOB-ANT v4. 1  (Granier et al. , 2019) , developed by joining CEDS trends and EDGAR v4. 3. 2 historical data, was used as a basis (hereinafter CAMS dataset), enriching it with locally developed inventories available in the bibliography until 2019, and selecting those with national coverage and with availability of data for the period and species of interest. The dataset presented in this work, hereinafter called PAPILA, focuses on the group of species known as reactive gases, given their relevance in atmospheric chemistry as precursors of O 3 and PM 2.
5 : carbon monoxide (CO), nitrogen oxides (NO x ), non-methane volatile organic compounds (NMVOCs), ammonia (NH 3 ) and sulfur dioxide (SO 2 )  (Sharma et al. , 2017). Due to the availability of data in the local AEIs and the completeness of the sectors represented, the 2014-2016 period was selected for this first version of the PAPILA dataset, including local information from Argentina  (Puliafito et al. , 2017; Castesana et al. , 2018) , Chile  (Mazzeo et al. , 2018; Gallardo et al. , 2018)  and Colombia  (IDEAM, 2017). In addition, a comparison of the and the sum of all categories (SUM). This grouping of activities and sectors was carried out following the CAMS sectoral disaggregation, except for the use of solvents, reported under IND in the PAPILA dataset. International navigation emissions were taken entirely from the CAMS database.
Agricultural fires were removed to allow the use of the inventories together with fire products such as GFEDv3 (van der  Werf et al. , 2010 ), FINNv1 (Wiedinmyer et al. , 2011)  and GFASv1  (Kaiser et al. , 2012) , avoiding double counting of these fires. It is worth mentioning that by "sum of all categories" we refer to all the sectors included in PAPILA, both for the presentation of our results and for comparative purposes with CAMS. A broader description of the activities contemplated under each category is presented in the Table  A1  in the Annex A, together with the equivalences with the IPCC 1996 reporting code. The PAPILA dataset  (Castesana et al. , 2021)  combines surface emissions from the comprehensive CAMS dataset with local information of those countries that, at the time of development of this emission inventory, had emission estimates of the mentioned species and covering the entire national territory: Argentina, Chile and Colombia.
Designing and building the PAPILA dataset Figure  2  presents a flow chart of the general methodology applied in combining local information with CAMS. Data from Argentina, Chile and Colombia was assessed in terms of species availability. In addition, and as described in the next subsections, the transparency on the methodology applied in emission estimates, and the representativeness and completeness of emission sectors were revised in line with the CAMS emission reporting system. For those species and/or categories with absence of local data, CAMS inventory was used to fill the gaps. One of the challenges of combining different local inventories to a common regional database is bringing them to a single, uniform and homogeneous grid. For this purpose, it was necessary to resolve all conflicts arising from cells shared by more than one country or coastal cells.
Both the activity data and their spatial distribution were based on the previous work by  Castesana et al. (2018 Castesana et al. ( , 2020)) , while the emission factors were those suggested by the EMEP according to the level of detail described for each activity in Table  1. These emissions have been estimated ad hoc to be included as part of the PAPILA dataset. Since they have not been published thus far, interested readers may find a more complete description of the results in the Annex A, including resulting emissions from fertilizers, crop production and animal excreta (dairy and beef cattle, poultry, swine, sheep, goats, horses and other livestock). Consistent with the referenced studies cited above, emissions from managed excreta are reported as AGL, and those deposited in pasture during animal grazing are reported under AGS.
Chile Chilean annual emissions were taken from the CR2-MMA dataset (CR2-MMA, 2018), based on the works of  Gallardo et al. (2018)  and  Mazzeo et al. (2018). Such dataset is presented with a spatial resolution of 0. 01°x 0. 01°, and includes 2014 emissions of reactive gases, GHG and particles, reported under the following aggregation: industries (which includes emissions from energy), urban and non-urban road transportation (which only includes CO and NO x for reactive gases), residential consumption, and agricultural and forest fires. Emission from industrial sources corresponds to the compilation of self-declared estimates by each facility to the Chilean Repository of Emissions and Pollutants Transport. Neither the methodology nor the emission factors used to estimate these emissions could be traced.
However, given that the local methodology for SO 2 emission estimates is based on sulfur content in fuels and in mass-flow balances in copper production processes, which constitute the main SO 2 emitter activity in Chile  (González-Rojas et al. , 2021) , we have considered that the information on sulfur content that is handled locally is reliable, and included the spatially distributed emissions as estimated in Chile in our dataset. For the rest of the species, we decided to exclusively adopt the spatial distribution and the share of the locally reported emissions and distributed the CAMS estimates by weighing them on the CR2-MMA spatial distribution as follows: E cell (i, j, k) = E local (i, j, k) N k=1 E CAMS (i, j, k) N k=1 E local (i, j, k) (2) where E cell (i, j, k) are the emissions of species i and sector j assigned to the cell grid k, N is the total number of grid cells covering the country, E local represents the emissions locally estimated and E CAMS the corresponding estimate from the global database.
Emission estimates from residential sources in the CR2-MMA dataset cover only firewood combustion for all species considered in our work  (Mazzeo et al. , 2018). According to local experts, one of the most relevant aspects of air quality in the coldest regions in southern Chile is the presence of CO, NMVOCs and particles from burning of firewood in households. Although this practice is included under the residential category of global inventories, the corresponding estimated emissions do not seem to be consistent with the magnitude of the air pollution situation observed at the local level. In addition, by downscaling global inventories in the Metropolitan Region of Santiago (MRS, Chile's central region)  Huneeus et al. (2020a)  found out that residential emissions were strongly overestimated in global databases and attributed this inaccuracy to the use of population density as a proxy for the spatial distribution.