Effects of stand composition on fire hazard in mixed‐wood Canadian boreal forest
Bibliographic record
Abstract
Abstract. Surface fuels were examined in 48 stands of the Canadian mixed‐wood boreal forest. Tree canopy was characterized with the point‐centred quadrant method and stands were characterized as deciduous, mixed‐deciduous, mixed‐coniferous or coniferous according to the percentage of conifer basal area. Woody debris loadings were measured with the line intersect method and the litter, duff, shrub loads and depths or heights were sampled with various quadrats. No significant difference was found among stand types for total woody debris load, large basal diameter shrub loads and load or depth of litter and duff. However, conifer stands had significantly heavier loads of small diameter elements (twigs and shrubs) and conifer pieces were more numerous within these stands than in deciduous stands. The BEHAVE prediction system was used to evaluate the impact of these differences on the potential of fire ignition in situations where topography and weather were constant. The qualitative and quantitative changes in fuels, resulting from species replacement and fast decay rates, influence fire hazard. Simulations of fire behaviour showed that in the mixed‐wood boreal forest fires were less intense and spread more slowly in deciduous stands than in mixed or coniferous stands. Moreover, spring fires were more intense than summer fires, and differences between seasons increased with the increase of deciduous basal area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".