An Evaluation of the British Columbia Asthma Monitoring System (BCAMS) and PM2.5 Exposure Metrics during the 2014 Forest Fire Season
Bibliographic record
Abstract
The British Columbia Asthma Monitoring System (BCAMS) tracks forest fire smoke exposure and asthma-related health outcomes, identifying excursions beyond expected daily counts. Weekly reports during the wildfire season support public health and emergency management decision-making. We evaluated BCAMS by identifying excursions for asthma-related physician visits and dispensations of the reliever medication salbutamol sulfate and examining their corresponding smoke exposures. A disease outbreak detection algorithm identified excursions from 1 July to 31 August 2014. Measured, modeled, and forecasted concentrations of fine particulate matter (PM2.5) were used to assess exposure. We assigned PM2.5 levels to excursions by choosing the highest value within a seven day window centred on the excursion day. Smoky days were defined as those with PM2.5 levels ≥ 25 µg/m3. Most excursions (57%-71%) were assigned measured or modeled PM2.5 concentrations of 10 µg/m3 or higher. Of the smoky days, 55.8% and 69.8% were associated with at least one excursion for physician visits and salbutamol dispensations, respectively. BCAMS alerted most often when measures of smoke exposure were relatively high. Better performance might be realized by combining asthma-related outcome metrics in a bivariate model.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".