Fuel treatments change forest structure and spatial patterns of fire severity, Arizona, U.S.A.
Bibliographic record
Abstract
Fuel reduction treatments are often designed to achieve multiple resource management objectives in addition to reducing potential fire hazard. In the White Mountains of Arizona State (U.S.A.), the 2014 San Juan Fire burned through several thinning prescriptions designed to achieve wildlife habitat objectives. Many studies have documented reduced fire severity for a standard set of fuel treatments, but the range of variability in fuel treatment effectiveness for alternative treatment designs is poorly understood. We used nonlinear mixed-effects modeling to estimate the distance into the treated area at which fire severity decreases and randomization tests to compare forest structure. High-severity fire effects were estimated to be reduced between 114 m and 345 m into the treated area. The range of variability in observed-distance high-severity fire effects persist into the treated area and, in conjunction with estimated relationships between posttreatment forest structure and severity, can inform the design of alternative fuel treatment prescriptions with various target prescriptions. We found that as cover was maintained in a treatment unit for wildlife habitat, the size of the fuel treatment necessary to observe a reduction in severity needs to be larger. Our study will inform decision makers on the size of treatments required to accomplish management objectives.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".