Evaluation of the BlueSky smoke forecasting system and its utility for public health protection in British Columbia
Bibliographic record
Abstract
Wildfire smoke is a major contributor to extreme particulate matter (PM) air pollution events and has been associated with respiratory and cardiovascular health effects. With climate change, more frequent and intense wildfires are expected in the future and their impact on public health will likely increase. The existing exposure assessment tools such as the monitoring network and remote sensing platforms have limitations for measuring wildfire smoke, including inadequate coverage and measuring total column instead of ground-level concentrations. From the public health perspective, a system that can supplement these tools and predict smoke concentrations will be valuable. The Western Canada BlueSky Smoke Forecasting System, which can predict PM₂.₅ (PM <2.5μm in diameter) from wildfires up to 60 hours in advance, has been developed since 2008. So far, there has not been any systematic and quantitative evaluation of its performance. The first objective of this study was to evaluate the performance of BlueSky. We compared its forecasting output with monitoring measurements and remote sensing images with several different model evaluation statistics of temporal and spatial agreement. The second objective was to assess the association between BlueSky predictions and respiratory health indicators. Poisson regression was employed between BlueSky predictions and the health outcome indicators, including counts of prescriptions dispensed to relieve respiratory health symptoms and counts of physician visits for asthma. Results suggested that BlueSky predicted smaller smoke plumes within the plumes observed by remote sensing. It predicted PM₂.₅ concentrations comparable to monitor measurements in the middle of the fire period and in areas consistently impacted by wildfire smoke. More frequent and larger-scale overpredictions were observed. A 30μg/m³ increase in BlueSky 24-hour PM₂.₅ predictions was associated with 1% increase in medication dispensations and physician visits for asthma. The relative risks were smaller than those associated with monitoring measurements. Smoke plume coverage predicted by BlueSky was associated with relative risks comparable with those observed by remote sensing. In conclusion, BlueSky predictions were comparable with measurements from other smoke assessment tools and they were significantly associated with respiratory health outcomes. This study provides evidence to support the use of BlueSky in public health protection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".