Evaluation of two risk mitigation strategies for dealing with fire-related uncertainty in timber supply modelling
Bibliographic record
Abstract
We embedded a linear programming timber harvest scheduling model into an aspatial stochastic simulation model of a flammable forest to evaluate two fire risk mitigation strategies. The harvest scheduling model is solved repeatedly to produce harvest schedules within a rolling planning horizon framework. The risk mitigation strategies we examined were (1) whether or not to account for fire in the planning model and (2) replanning interval. We evaluated those strategies under four representative fire regimes. We found that accounting for fire in the planning model reduced the harvest volume variability as fire activity increased (i.e., for average annual burn fractions ≥0.45%), but replanning intervals over a range of 1 to 10 years had little impact on harvest volume variability. We also developed a risk analysis decision-making aid that forest managers can use to help deal with fire-related uncertainty. Our results suggest that risk-averse forest managers should account for fire while planning, especially when burn fractions exceed 0.45%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".