Equilibrium forest age structure: Simulated effects of random wild fires, fire control, and harvesting
Bibliographic record
Abstract
Historically, fire has been one of the main determinants of age structure in the forests of British Columbia, but in the face of recurring fires and other disruptive processes, achieving a stable forest age-class structure for sustainable harvesting is a challenge. The present paper investigates possible interactions of fire with harvesting and fire control, and their effects on age structure of a pine forest with varying fire-cycle lengths and fire-size regimes. We used simulation to determine the effects of the frequency and size of fires, fire control, and harvesting on the equilibrium age distribution of a forest. For small fires, resulting equilibrium age-class distributions were all declining, whereas for large fires, equilibrium was never achieved. Volume available for harvest was much greater when fires were infrequent. Harvest increased with fire control, but decreased with harvest age. In this simulation, the combination of intensive fire control and early harvesting optimized wood volume production. Awareness of the implications of particular fire regimes on sustained forest yield can inform design of better forest management and fire protection strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".