Pyrosilviculture: Combining prescribed fire with gap-based silviculture in mixed-conifer forests of the Sierra Nevada
Bibliographic record
Abstract
We used a prescribed fire study to demonstrate the concept of pyrosilviculture, defined here as (i) using prescribed fire to meet management objectives or (ii) altering nonfire silvicultural treatments explicitly in order to optimize the incorporation of prescribed fire in the future. The study included implementation of relatively hot prescribed burns in mixed-conifer forests that have been managed with gap-based silviculture. The fires burned through 12-, 22-, 32-, and 100-year-old cohorts, thus enabling an analysis of stand age influences on fire effects. Mastication and precommercial thinning were assessed as prefire treatments in the 12-year-old stands. Postburn mortality and crown scorch declined with stand age. There was a clear tradeoff between fuel consumption and high rates of tree damage and mortality in the 12-year-old stands. Masticated stands had higher levels of mean crown scorch (78%) compared with precommercially thinned stands (52%). Mortality for all 12-year-old stands was high, as nearly half of the trees were dead 1 year after the fires. Giant sequoia and ponderosa pine had relatively high resistance to fire-related mortality. When applying the concept of pyrosilviculture, there may be opportunities to combine prescribed fire with regeneration harvests that create a variety of gap sizes to sustain both low fire hazard and promote structural heterogeneity and sustainable age structures that may not be achieved with prescribed fire alone.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".