Forest management strategies for dealing with fire-related uncertainty when managing two forest seral stages
Bibliographic record
Abstract
Ecological values are an important aspect of sustainable forest management, but little attention has been paid to maintaining these values when using traditional linear programming (LP) forest management planning models in uncertain planning environments. We embedded an LP planning model that specifies when and how much to harvest in a simulation model of a “managed” flammable forest landscape. The simulation model was used to evaluate two strategies for dealing with fire-related uncertainty when managing mature and old forest areas. The two seral stage areas were constrained in the LP planning model to a minimum of 10% of the total forest area and the strategies were evaluated under four representative fire regimes. We also developed a risk analysis tool that can be used by forest managers that wish to incorporate fire-related uncertainty in their decision-making. We found that use of the LP model would reduce the areas of the mature and old forest to their lower bound and fire would further reduce the seral areas below those levels, particularly when the mean annual burn fraction exceeds 0.45% per annum. Increasing the minimum area required (i.e., the right-hand side of the constraint) would increase the likelihood of satisfying the minimum area requirements.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".