Modelling emissions from Canadian wildfires: a case study of the 2002 Quebec fires
Bibliographic record
Abstract
The present paper proposes an original approach to estimate gaseous and particulate emissions from boreal forest fires based on the Canadian Forest Fire Behaviour Prediction (FBP) System. The FBP System permits calculation of fuel consumption and rate of spread for individual fires on an hourly basis from meteorological conditions and fuel patterns. Weather data are obtained by running the Canadian weather forecast model GEM (Global Environmental Multiscale). Hourly emission point sources can then be generated from a given wildfire database. The smoke emission model was first applied to the boreal forest fires in Quebec in the summer of 2002. Geographical distribution and temporal variability of emission amounts, as well as injection heights, were assessed hourly. In July, ~150 wildfires released 39 Mt of CO2 equivalent of greenhouse gases and 470 kt of fine particulate matter to the atmosphere. They contributed 32 and 5% of Quebec’s and Canada’s annual greenhouse gas emissions, respectively. Black carbon was estimated to account for 4% of the total fine particulate matter. Wildfires were responsible for 51 and 90% of all Canada’s black carbon and particulate organic matter sources, respectively.
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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 | 0.001 | 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 | 0.000 |
| 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".