MétaCan
Menu
Back to cohort
Record W2785649357 · doi:10.1126/science.aaq0524

Volatile chemical products emerging as largest petrochemical source of urban organic emissions

2018· article· en· W2785649357 on OpenAlexafffund
Brian McDonald, J. A. de Gouw, J. B. Gilman, Shantanu H. Jathar, Ali Akherati, Christopher D. Cappa, J. L. Jiménez, J. Lee‐Taylor, Patrick L. Hayes, S. A. McKeen, Y. Y. Cui, Si‐Wan Kim, Drew R. Gentner, Gabriel Isaacman‐VanWertz, Allen H. Goldstein, Robert A. Harley, G. J. Frost, J. M. Roberts, Thomas B. Ryerson, M. Trainer

Bibliographic record

VenueScience · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesAlfred P. Sloan FoundationNational Aeronautics and Space AdministrationNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsPetrochemicalEnvironmental sciencePollutionEnvironmental chemistryAir pollutionOzoneChemical industryWaste managementEnvironmental protectionChemistryEnvironmental engineeringEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

A gap in emission inventories of urban volatile organic compound (VOC) sources, which contribute to regional ozone and aerosol burdens, has increased as transportation emissions in the United States and Europe have declined rapidly. A detailed mass balance demonstrates that the use of volatile chemical products (VCPs)-including pesticides, coatings, printing inks, adhesives, cleaning agents, and personal care products-now constitutes half of fossil fuel VOC emissions in industrialized cities. The high fraction of VCP emissions is consistent with observed urban outdoor and indoor air measurements. We show that human exposure to carbonaceous aerosols of fossil origin is transitioning away from transportation-related sources and toward VCPs. Existing U.S. regulations on VCPs emphasize mitigating ozone and air toxics, but they currently exempt many chemicals that lead to secondary organic aerosols.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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 designObservational
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

Citations1,377
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueScienceSame topicAtmospheric chemistry and aerosolsFrench-language works237,207