The Canadian Optimized Statistical Smoke Exposure Model (CanOSSEM): A machine learning approach to estimate national daily fine particulate matter (PM2.5) exposure
Bibliographic record
Abstract
Exposure to biomass smoke has been associated with a wide range of acute and chronic health outcomes. Over the past decades, the frequency and intensity of wildfires has increased in many areas, resulting in longer smoke episodes with higher concentrations of fine particulate matter (PM2.5). There are also many communities where seasonal open burning and residential wood heating have short- and long-term impacts on ambient air quality. Understanding the acute and chronic health effects of biomass smoke exposure requires reliable estimates of PM2.5 concentrations during the wildfire season and throughout the year, particularly in areas without regulatory air quality monitoring stations. We have developed a machine learning approach to estimate PM2.5 across all populated regions of Canada from 2010 to 2019. The random forest machine learning model uses potential predictor variables integrated from multiple data sources and estimates daily mean (24-hour) PM2.5 concentrations at a 5 km × 5 km spatial resolution. The training and prediction datasets were generated using observations from National Air Pollution Surveillance (NAPS) network. The Root Mean Squared Error (RMSE) between predicted and observed PM2.5 concentrations was 2.96 μg/m3 for the entire prediction set, and more than 96 % of the predictions were within 5 μg/m3 of the NAPS PM2.5 measurements. The model was evaluated using 10-fold, leave one-region-out, and leave-one-year-out cross-validations. Overall, CanOSSEM performed well but performance was sensitive to removal of large wildfire events such as the Fort McMurray interface fire in May 2016 or the extreme 2017 and 2018 wildfire seasons in British Columbia. Exposure estimates from CanOSSEM will be useful for epidemiologic studies on the acute and chronic health effects associated with PM2.5 exposure, especially for populations affected by biomass smoke where routine air quality measurements are not available.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".