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Record W2580788351 · doi:10.1073/pnas.1612430114

Transient climate and ambient health impacts due to national solid fuel cookstove emissions

2017· article· en· W2580788351 on OpenAlexaff
Forrest Lacey, Daven K. Henze, Colin J. Lee, Aaron van Donkelaar, Randall V. Martin

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsDalhousie University
FundersU.S. Environmental Protection AgencyNational Aeronautics and Space Administration
KeywordsTransient (computer programming)Environmental scienceClimate changeMeteorologyWaste managementEngineeringGeographyComputer science

Abstract

fetched live from OpenAlex

Significance Widespread use of solid fuels for cooking results in a significant source of anthropogenic emissions. Of foremost concern for indoor air quality, reductions to these emissions could also impact both climate and ambient air quality. These potential cobenefits are appealing to efforts aimed at reducing cookstove emissions on national to urban scales, but have yet to be comprehensively evaluated at these scales. We thus estimate the per cookstove impacts on ambient air quality and global mean surface temperature for every individual country with significant cookstove use, considering reductions to both aerosols and long-lived greenhouse gases over the next century. This estimation provides information for policy makers evaluating climate and ambient air quality cobenefits of cookstove intervention programs worldwide.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.325
Teacher spread0.286 · 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

Citations140
Published2017
Admission routes1
Has abstractyes

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