The risks to human health of air toxics, PM2.5, and ozone from the 2023 Canadian wildfires
Bibliographic record
Abstract
The 2023 Canadian wildfires resulted in substantial emissions to air. In this work, we used predicted concentrations of hazardous air pollutants (HAPs), fine particulate matter (PM2.5), and ozone across the U.S. and Canada to estimate the potential impacts of 2023 Canadian wildfire smoke on human health. HAPs from Canadian wildfires were estimated to increase lifetime population-weighted cancer inhalation risk by 1-in-1 million and noncancer risk by a hazard index (HI) of 0.05 across the domain. The additional risk from HAPs in smoke was predicted to be above a cancer risk of 100-in-1 million for 1,300 people and a HI of 1.0 for 110,000 people, all in Canada. In addition, 360,000 people in Canada were predicted to experience an increase in annual-mean PM2.5 of at least 5 micrograms per m3. Nearly 63 million people, 76% of which were in the U.S., were predicted to experience an increase in seasonal maximum daily 8-hour average ozone greater than 1 ppb. In Canada, ozone and PM2.5 associated with the 2023 fires were estimated to result in over 3x more attributable deaths per year than wildfire smoke in years 2013 through 2018.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".