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Record W2029779422 · doi:10.1016/j.envpol.2013.11.007

Risk assessment for cardiovascular and respiratory mortality due to air pollution and synoptic meteorology in 10 Canadian cities

2013· article· en· W2029779422 on OpenAlexafffundabout
Jennifer Vanos, Chris Hebbern, Sabit Cakmak

Bibliographic record

VenueEnvironmental Pollution · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsHealth Canada
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsAir pollutionEnvironmental sciencePoisson regressionAir quality indexNitrogen dioxideOzoneMeteorologyEnvironmental healthClimatologyRelative riskAtmospheric sciencesGeographyMedicinePopulationBiologyEcology

Abstract

fetched live from OpenAlex

Synoptic weather and ambient air quality synergistically influence human health. We report the relative risk of mortality from all non-accidental, respiratory-, and cardiovascular-related causes, associated with exposure to four air pollutants, by weather type and season, in 10 major Canadian cities for 1981 through 1999. We conducted this multi-city time-series study using Poisson generalized linear models stratified by season and each of six distinctive synoptic weather types. Statistically significant relationships of mortality due to short-term exposure to carbon monoxide, nitrogen dioxide, sulphur dioxide, and ozone were found, with significant modifications of risk by weather type, season, and mortality cause. In total, 61% of the respiratory-related mortality relative risk estimates were significantly higher than for cardiovascular-related mortality. The combined effect of weather and air pollution is greatest when tropical-type weather is present in the spring or summer.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.104
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.267
Teacher spread0.242 · 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 teacher head, 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

Citations126
Published2013
Admission routes3
Has abstractyes

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