Historical disturbance regimes as a reference for forest policy in a multiowner province: a simulation experiment
Bibliographic record
Abstract
Using a landscape simulation model, we examined ecological and economic implications of forest policies designed to emulate the historical fire regime across the 2 × 106 ha Oregon Coast Range. Simulated policies included two variants of the current policy and three policies reflecting aspects of the historical fire regime. Policy development was guided by the management intentions of four owner groups: forest industry, nonindustrial private, state, and federal. Fire severity was emulated with green-tree retention standards; fire frequency was emulated with annual harvestable area restrictions; and fire extent was emulated with harvest-unit size regulations. Simulated disturbance-based policies produced age-class distributions closer to the estimated historical range than those created by the current policy. Within 100 years, proportions of younger forests were within the historical range, while older forests moved closer to, but remained below, historical conditions. In the near term, disturbance-based policies produced annual harvest volumes 20%60% lower than those produced by the current policy. However, relative costs of disturbance-based policies diminished over time. Our results suggest that if expediting a return to historical age-class distributions at a provincial-scale was a goal, then public lands would be needed to provide large patches of old forest. In addition, this experiment illustrated that distributing costs and benefits of conservation policies equitably across multiple private landowners is a significant challenge.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".