Fuel treatments reduce the severity of wildfire effects in dry mixed conifer forest, Washington, USA
Bibliographic record
Abstract
To address hazardous fuel accumulations, many fuel treatments are being implemented in dry forests, but there have been few opportunities to evaluate treatment efficacy in wildfires. We documented the effectiveness of thinning and prescribed burning in the 2006 Tripod Complex fires. Recent fuel treatments burned in the wildfires and offered an opportunity to evaluate if two treatments (thin only and thin and prescribed burn) mitigated fire severity. Fire severity was markedly different between the two treatments. Over 57% of trees survived in thin and prescribed burn (thinRx) units versus 19% in thin only (thin) and 14% in control units. Considering only large-diameter trees (>20 cm diameter at breast height), 73% survived in thinRx units versus 36% and 29% in thin and control units, respectively. Logistic regression modeling demonstrates significant reductions in the log-odds probability of tree death under both treatments with a much greater reduction in thinRx units. Other severity measures, including percent crown scorch and burn severity index, are significantly lower in thinRx units than in thin and control units. This study provides strong quantitative evidence that thinning alone does not reduce wildfire severity but that thinning followed by prescribed burning is effective at mitigating wildfire severity in dry western forests.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".