Dead and down woody debris fuel loads in Canadian forests
Bibliographic record
Abstract
In Canada, fire behaviour is modelled based on a fuel classification system of 16 fuel types. Average fuel loads are used to represent a wide range of variability within each fuel type, which can lead to inaccurate predictions of fire behaviour. Dead and down woody debris (DWD) is a major component of surface fuels affecting surface fuel consumption, potential crown fire initiation, and resulting crown fuel consumption and overall head fire intensity. This study compiled a national database of DWD fuel loads and analysed it for predictive driving variables. The database included DWD fuel loads for all dominant Canadian forest types at three size classes: fine (<1 cm), medium (1–7 cm) and coarse (>7 cm). Predictive models for DWD fuel load by size classes individually and collectively for various forest types and ecozones were analysed. Bioclimatic regime, age, spatial position, drainage, and structural components including diameter at breast height and stem density were significant variables. This study provides tools to improve our understanding of the spatial distribution of DWD across Canada, which will enhance our ability to represent its contribution within fire behaviour and fire effects models.
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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 | 0.002 | 0.002 |
| 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".