Machine Learning Approach To Estimate Hourly Exposure to Fine Particulate Matter for Urban, Rural, and Remote Populations during Wildfire Seasons
Bibliographic record
Abstract
Exposure to wildfire smoke averaged over 24-hour periods has been associated with a wide range of acute cardiopulmonary events, but little is known about the effects of sub-daily exposures immediately preceding these events. One challenge for studying sub-daily effects is the lack of spatially and temporally resolved estimates of smoke exposures. Inexpensive and globally applicable tools to reliably estimate exposure are needed. Here we describe a Random Forests machine learning approach to estimate 1-hour average population exposure to fine particulate matter during wildfire seasons from 2010 to 2015 in British Columbia, Canada, at a 5 km × 5 km resolution. The model uses remotely sensed fire activity, meteorology assimilated from multiple data sources, and geographic/ecological information. Compared with observations, model predictions had a correlation of 0.93, root mean squared error of 3.2 μg/m 3, mean fractional bias of 15.1%, and mean fractional error of 44.7%. Spatial cross-validation indicated an overall correlation of 0.60, with an interquartile range from 0.48 to 0.70 across monitors. This model can be adapted for global use, even in locations without air quality monitoring. It is useful for epidemiologic studies on sub-daily exposure to wildfire smoke and for informing public health actions if operationalized in near-real-time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".