Using Air Quality Alerts to Estimate Population-Based Wildfire Smoke Exposure from the 2023 Canadian Wildfire Season
Bibliographic record
Abstract
Wildfires are a source of air pollution, which impacts air quality in proximity to and at great distances from fires. Wildfire smoke exposure is seasonal and episodic, with exposure levels and durations that can vary considerably. Exposure to wildfire smoke is associated with numerous health effects, including an increased risk of mortality and exacerbation of respiratory diseases. In Canada, the health risks of wildfire smoke are communicated to the public via air quality (AQ) alerts, when levels of wildfire smoke are currently or are forecasted to be relatively high, posing a risk to the general population. To better understand the population at risk due to wildfire smoke, a population-based exposure metric was developed based on geolocated AQ alerts and population data. This metric, measured in person-days, quantifies the number of people at risk of experiencing adverse health effects of wildfire smoke during a given time period. Data from the 2023 wildfire season were used to evaluate the metric. The greatest numbers of person-days were associated with population centres and regions that experienced periods of prolonged, intense smoke exposure. For example, Toronto, a large population centre, had 12 days with AQ alerts issued, corresponding to 33.5 M person-days. This approach could be expanded to other environmental or extreme weather conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".