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Record W4296001808 · doi:10.1088/1748-9326/ac9009

Historical transboundary ozone health impact linked to affluence

2022· article· en· W4296001808 on OpenAlexaff
Lulu Chen, Jintai Lin, Ruijing Ni, Hao Kong, Mingxi Du, Yingying Yan, Mengyao Liu, Jingxu Wang, Hongjian Weng, Yuanhong Zhao, Chunjin Li, Randall V. Martin

Bibliographic record

VenueEnvironmental Research Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsDalhousie University
FundersNational Natural Science Foundation of China
KeywordsOzoneSocioeconomic statusEnvironmental scienceOzone depletionGeographyTurning pointEnvironmental protectionPollutionNatural resource economicsEnvironmental healthMeteorologyEconomicsPopulationEcologyMedicine

Abstract

fetched live from OpenAlex

Abstract Ozone pollution is a major transboundary threat to global health. Systematic improvement of mitigation strategy for transboundary ozone requires a socioeconomic understanding of historical lessons in countries at different affluence levels. Here, we explore the changes in transboundary ozone related premature deaths over 1951–2019 driven by anthropogenic emissions of four country groups categorized by income level. By integrating global emission datasets, a chemical transport model (GEOS-Chem), in situ ozone measurements worldwide and an ozone exposure-response model, we find that mortality caused by transboundary anthropogenic ozone increases by 27 times from 1951 to 2019, and on average contributes about 27% of global anthropogenic ozone related deaths. All groups exert and suffer from substantial transboundary ozone related mortality. The high-income and upper middle groups have each experienced an inverted U-shaped relationship between its affluence and per-million-people contribution to mortality caused by transboundary ozone, with the turning point around 23 000 USD and 6300 USD, respectively. The lower middle group has gradually matched the growth pathway of the upper middle group with a turning point less clear. Concerted efforts to ensure early turning points in less affluent countries will have considerable global health benefits.

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 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.002

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 head0.086
GPT teacher head0.387
Teacher spread0.302 · 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
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

Citations14
Published2022
Admission routes1
Has abstractyes

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