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Record W3211296383 · doi:10.5281/zenodo.4739100

GBD-MAPS-Global: Gridded Fractional Source Contribution Results

2021· dataset· en· W3211296383 on OpenAlexaff
Erin E. McDuffie, Michael Bräuer, Randall V. Martin, Melanie S. Hammer, Aaron van Donkelaar, Steven J. Smith, Patrick O’Rourke, Liam Bindle, Viral Shah, Lyatt Jaeglé, Gan Luo, Fangqun Yu, Jamiu Adetayo Adeniran, Jintai Lin, Joseph V. Spadaro, Richard T. Burnett

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typedataset
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsDalhousie UniversityUniversity of British Columbia
Fundersnot available
KeywordsGeologyComputer science

Abstract

fetched live from OpenAlex

<strong>Overview</strong><br> This dataset contains the modeled gridded fractional source contribution results for the 2021 Global Burden of Disease - Major Air Pollution Sources (GBD-MAPS) - Global study. Fractional results aggregated to the regional, national, and sub-national scales are available as Supplementary Datasets in McDuffie et al., Nature Communications, 2021. This GBD-MAPS-Global methodology and results are described in the following article:<br> McDuffie, E. E., Martin, R. V., Spadaro, J. V., Burnett, R., Smith, S. J., O'Rourke, P., Hammer, M., van Donkelaar, A., Bindle, L., Shah, V., Jaegle, L., Luo, G., Yu, F., Adeniran, J., Lin, J., Brauer, M. Source Sector and Fuel Contributions to Ambient PM<sub>2.5</sub> Attributable Mortality Across Multiple Spatial Scales, <em>Nature Communications</em> <strong>Notes:</strong><br> All results provide the modeled fractional contribution of each designated source category to total surface PM<sub>2.5</sub> mass and attributable disease burden. Fractional source contribution results are derived from emission sensitivity simulations using the GEOS-Chem 3D global chemical transport model (DOI: 10.5281/zenodo.4718622) and emissions from the Community Emissions Data System (GBD-MAPS update) (DOI: 10.5281/zenodo.3754964), unless otherwise noted. Results are provided at the 0.01°x0.01° (~1 km x 1 km) spatial resolution and are based on original simulations conducted at 2°x2.5° (global) and 0.5°x0.625° (Europe, North America, and Asia regions) degree resolutions. All units are in fractional percentages (e.g., 0.0245 is 2.45%). To calculate gridded <em>absolute</em> contributions of each source to PM<sub>2.5</sub> mass (used in the GBD-MAPS analysis):<br> 1) Multiply the 0.01°x0.01° fractional source contributions by downscaled 0.01°x0.01° resolution PM<sub>2.5</sub> mass estimates, available in the DownscaledPM.zip folder as part of the GBD-MAPS-Global: Analysis Input Dataset (DOI: 10.5281/zenodo.4642700) <strong>Data File Descriptions:</strong><br> <strong>LatLon</strong> - The latitude and longitude values that correspond to each data point in the source-specific files (0.01°x0.01° resolution) Sector Files (sum to 100%): <strong>AFCID</strong> - <em>Anthropogenic Fugitive, Combustion, and Industrial Dust</em><br> <strong>AGR</strong> - <em>Agriculture</em> - includes manure management, soil fertilizer emissions, rice cultivation, enteric fermentation, and other agriculture<br> <strong>ENEcoal</strong> - <em>Energy Production (coal combustion only)</em> - Includes electricity and heat production, fuel production and transformation, oil and gas fugitive/flaring, and fossil fuel fires<br> <strong>ENEother</strong> - <em>Energy Production (all non-coal combustion) </em>- Includes electricity and heat production, fuel production and transformation, oil and gas fugitive/flaring, and fossil fuel fires<br> <strong>GFEDagburn</strong> - <em>Agricultural Waste Burning </em>- Includes solid waste disposal, waste incineration, waste-water handling, and other waste handling (from the GFED fires inventory)<br> <strong>GFEDoburn </strong>- <em>Other Open Fires</em> - Includes deforestation, boreal forest, peat, savannah, and temperate forest fires (from the GFED fires inventory) <br> <strong>INDcoal</strong> - <em>Industry (coal combustion only)</em> - Includes Industrial combustion (iron and steel, non-ferrous metals, chemicals, pulp and paper, food and tobacco, non-metallic minerals, construction, transportation equipment, machinery, mining and quarrying, wood products, textile and leather, and other industry combustion) and non-combustion industrial processes and product use (cement production, lime production, other minerals, chemical industry, metal production, food, beverage, wood, pulp, and paper, and other non-combustion industrial emissions)<br> <strong>INDother</strong> - <em>Industry (all non-coal combustion)</em> - Includes Industrial combustion (iron and steel, non-ferrous metals, chemicals, pulp and paper, food and tobacco, non-metallic minerals, construction, transportation equipment, machinery, mining and quarrying, wood products, textile and leather, and other industry combustion) and non-combustion industrial processes and product use (cement production, lime production, other minerals, chemical industry, metal production, food, beverage, wood, pulp, and paper, and other non-combustion industrial emissions)<br> <strong>NRTR</strong> - <em>non-road/ off-road transportation </em>- Includes Rail, Domestic navigation, Other transportation<br> <strong><em>OTHER</em></strong><em> - all remaining sources</em>, including: volcanic SO2, lightning NOx, biogenic soil NO, ocean emissions, biogenic emissions, very short lived iodine and bromine species, decaying plants (misc. inventories)<br> <strong>RCOC</strong> - <em>Commercial Combustion</em> - Includes commercial and institutional combustion<br> <strong>RCOO</strong> - <em>Other Combustion</em> - Includes combustion from agriculture, forestry, and fishing<br> <strong>RCORbiofuel</strong> - <em>Residential combustion (solid biofuel combustion only) </em>- includes residential heating and cooking<br> <strong>RCORcoal</strong> - <em>Residential combustion (coal combustion only)</em> - includes residential heating and cooking<br> <strong>RCORother</strong> - <em>Residential Combustion (all non-coal and non-solid biofuel)</em> - includes residential heating and cooking<br> <strong>ROAD</strong> - <em>Road Transportation</em> - includes cars, motorcycles, heavy and light duty trucks and buses<br> <strong>SHP</strong> - <em>International Shipping</em> - Includes international shipping and tanker loading<br> <strong>SLV</strong> - <em>Solvents</em> - Includes solvents production and application (degreasing and cleaning, paint application, chemical products manufacturing and processing, and other product use)<br> <strong>WDUST</strong> - <em>Windblown Dust - </em>(from the DEAD dust model)<br> <strong>WST</strong> - <em>Waste</em> - Includes solid waste disposal, waste incineration, waste-water handling, and other waste handling Fuel Categories (do not sum to 100% for each grid cell as these only include combustion sources of PM<sub>2.5</sub>): <strong>BIOFUEL</strong> - <em>Solid Biofuel</em> (or biomass) <em>Combustion</em>- Includes solid biofuel<br> <strong>COAL</strong> - <em>Total Coal</em> <em>Combustion </em>- Includes hard coal, brown coal, coal coke<br> <strong>OILGAS- </strong><em>Liquid Oil and Natural Gas Combustion </em>- Includes light and heavy oil, diesel oil, and natural gas

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.262
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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