Comparison modelling of PM2.5 concentrations in the 2023 Canadian Wildfires between various emissions data products (GBBEPx, GFAS, FEER) in HYSPLIT
Bibliographic record
Abstract
Wildfires are common dry-season occurrence in forested areas and cause various air quality and public health issues. To supplement the risk management of such wildfires, we ran a case study of the 2023 Canadian wildfires. The 2023 Canadian wildfire was a product of a dry late spring, ,which eleas to record fires. In this study, we run the HYSLPIT models with different fire emissions (GBBEPx, FEER, CFAS, etc) and different plume rise schemes (Briggs and Sofiev) to study the impact of emission and plume rise estimation on wildfire air quality forecast. To determine the accuracy of each sensitivity experiment, we compared the PM2.5 concent6rations from the HYSPLIT runs with ground measurements from AirNow stations across the study areas of New York, Philadelphia, and Washington DC. We found that when using the GFAS emission we would get the highest PM2.5 concentration readings, up to 108 μg/m2 in New York. However, when compared to the AirNOW, PM2.5 station readings we found that the GFAS readings were consistently several hours late. Out of all emissions, we found that FEER reported the smallest PM2.5 concentrations as they never reached above 25 μg/m2.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".