Quantifying the effect of fuel reduction treatments on fire behavior in boreal forests
Bibliographic record
Abstract
Mechanical (e.g., shearblading) and manual (e.g., thinning) fuel treatments have become the preferred strategy of many fire managers and agencies for reducing fire hazard in boreal forests. This study attempts to characterize the effectiveness of four fuel treatments through direct measurement of fire intensity and forest floor consumption during a single prescribed burn. The treatments included (1) thinning trees and removing debris (THIN-REMOVE-1 and -2), (2) thinning trees and burning the debris onsite, (3) shearblading and leaving the debris in place (SHEAR), and (4) shearblading and piling the debris in windrows (SHEAR-ROW). Fire burned through treatments 1, 3, and 4 and one control unit. The highest fire intensities (maximum temperature of 1150 °C, maximum heat flux of 227 kW·m–2, and fire cumulative energy release of 4277 J·m–2) were measured in the control. Treatment 1 exhibited a peak temperature of 267 °C, peak heating of 16 kW·m–2, and cumulative energy release of 2600 J·m–2. Treatments 3 and 4 exhibited peak temperatures of 170 and 66 °C, peak heating of 51 and 3 kW·m–2, and cumulative energy release of 2500 and 1800 J·m–2, respectively. The thinned treatment showed the most significant impact in the context of reduced heat release.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".