Post-wildfire salvage logging effects on snag structure and dead woody fuel loadings
Bibliographic record
Abstract
Salvage logging is a controversial tool for post-wildfire management that removes fire-killed trees. We use a generalized randomized experimental design to fulfill two main objectives: (1) quantify the immediate (1-year post-harvest) effects of salvage logging on stand structure, fine and coarse woody fuel loadings; and (2) use pre- and post-empirical field data and the Fire and Fuels Extension to the Forest Vegetation Simulator (Reinhardt and Crookston 2003) to simulate post-wildfire dead woody fuel succession and snag dynamics. We compared the effects on woody fuel loadings of two salvage logging prescriptions: (1) seed tree harvest, thin to 3.4 m2·ha−1; and (2) full salvage of all merchantable timber, relative to unlogged controls. There was substantial block-level variability in the implementation of the treatments and in their immediate effects on fine fuel loading, complicating comparison of the two prescriptions. Overall, salvage logging did reduce snag basal area and, relative to unlogged controls, significantly increased measured fine woody fuel loading (10 and 100 h). Simulated snag fall was rapid, with a mean predicted snag basal area loss of 61% within 10 years. Future long-term monitoring of permanent field plots will supplement model predictions and provide valuable data to inform post-wildfire management decisions.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".