Crown fuel consumption in Canadian boreal forest fires
Bibliographic record
Abstract
Predictive crown fuel consumption models were developed using empirical data from experimental burning projects. Crown fuel load for foliage, bark, branchwood and stemwood were calculated for live overstorey and understorey trees in each plot using nationally derived tree biomass algorithms. Standing dead tree branchwood and stemwood biomass were similarly calculated. Crown bulk density values were calculated for all non-stemwood fuel components. Factors that affect the initiation and spread of crown fires (live crown base height, foliar moisture content, surface fuel consumption, critical surface fire spread rate, critical surface fire intensity) and components of the Canadian Forest Fire Weather Index System were not statistically significant variables. Crown bulk density was moderately correlated with crown fuel consumption but was not an influential factor. A new crown fuel consumption model was developed by regression analysis using fuel load of overstorey tree foliage and standing dead tree branchwood, and fire rate of spread through crown fraction burned. A simpler model was developed using only overstorey tree foliage fuel load and fire rate of spread. Both models provide forest and fire management agencies with enhanced ability to determine crown fuel consumption, fire behaviour and carbon emissions in boreal fires using basic forest inventory or biomass/carbon datasets.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".