Differential and combined impacts of extreme temperatures and air pollution on human mortality in south–central Canada. Part I: historical analysis
Bibliographic record
Abstract
This paper forms the first part of an introduction to a synoptic weather typing approach to assess differential and combined impacts of extreme temperatures and air pollution on human mortality in south–central Canada, focusing on historical analysis (a companion paper—Part II focusing on future estimates). In this study, an automated synoptic weather typing procedure was used to identify weather types that have a marked association with high air pollution levels and temperature extremes, and facilitates assessments of the differential and combined health impacts of extreme temperatures and air pollution. Annual mean elevated mortality (when daily mortality exceeds the baseline) associated with extreme temperatures and acute exposures to air pollution, based on 1954–2000, was 1,082 [95% confidence interval (CI) of 1,017–1,147] for Montreal, 1,047 (CI 994–1,100) for Toronto, 462 (CI 438–486) for Ottawa, and 327 (CI 311–343) for Windsor. Of this annual mean elevated mortality, extreme temperatures are usually associated with roughly 20%, while air pollution is associated with the remaining 80%. Three pollutants (ozone, sulfur dioxide, and nitrogen dioxide) are associated with approximately 75% of total air pollution-related mortality across the study area. The remaining 25% is almost evenly associated with suspended particles and carbon monoxide, the other two pollutants addressed in this study. Of the five pollutants, ozone is most significantly associated with elevated mortality, making up one-third of the total air pollution-related mortality. PM 2.5 and PM 10 were not used as a measure of particulate in the study due to brief data records. The study results also suggest that, on the basis of daily mortality risks, extreme temperature-related weather presents a much greater risk to human health during heat waves and cold spells than air pollution. For example, in Montreal and Toronto, daily mean elevated mortality counts within the hottest weather type were twice as high as those within air pollution-related weather types.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".