Incorporating average and maximum area restrictions in harvest scheduling models
Bibliographic record
Abstract
A major goal in natural resource management has long been balancing the multiple uses of forest lands. Timber harvesting remains an important component of natural resource utilization, but must be approached in such a way that recreational use, ecosystem dynamics, species survivability, and other considerations are not sacrificed. One way in which production impacts are mitigated in forest management is by restricting the spatial extent of harvesting activities in developed plans. Through the use of harvest scheduling optimization models, constraints can be structured and imposed to limit local area disturbance associated with harvesting to a stipulated maximum. This represents an approach for regulating impacts in an economically driven management setting. Harvest scheduling research has recognized the challenges in appropriately structuring maximum area restrictions in optimization models, but regulating average disturbance area size may also be desired. This paper develops a model formulation for imposing average and maximum area limits on local impacts in harvest scheduling that can be solved using exact techniques. Application results are presented that highlight the feasibility of this approach. Further, the associated tradeoffs that exist in modeling both average and maximum area restrictions simultaneously are illustrated.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".