MétaCan
Menu
Back to cohort
Record W4400267603 · doi:10.1016/s2542-5196(24)00098-6

Global health costs of ambient PM2·5 from combustion sources: a modelling study supporting air pollution control strategies

2024· article· en· W4400267603 on OpenAlexaff
Hao Yin, Erin E. McDuffie, Randall V Martin, Michael Bräuer

Bibliographic record

VenueThe Lancet Planetary Health · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of ChinaNational Aeronautics and Space AdministrationHealth Effects InstituteUniversity of MarylandUniversity of WashingtonU.S. Environmental Protection Agency
KeywordsParticulatesAir quality indexAir pollutionEnvironmental scienceCombustionControl (management)PollutionEnvironmental planningBusinessEnvironmental healthEnvironmental resource managementComputer scienceMeteorologyGeographyChemistryEcologyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: ) from combustion sources can guide policy design for both air pollution and climate mitigation efforts. METHODS: from six major combustion sources across 204 countries using updated concentration-response models and an age-adjusted valuation method. We defined major combustion sources as the sum of total coal, liquid fuel and natural gas, solid biofuel, agricultural waste burning, other fires, and 50% of the anthropogenic fugitive, combustion, and industrial dust source. FINDINGS: from the abatement of solid biofuel emissions. INTERPRETATION: concentration. FUNDING: The Health Effects Institute, the National Natural Science Foundation of China, and NASA.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.052
GPT teacher head0.342
Teacher spread0.290 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueThe Lancet Planetary HealthSame topicAir Quality and Health ImpactsFrench-language works237,207