Optimality of continuous cover vs. clear-cut regimes in managing forest resources
Bibliographic record
Abstract
Optimization models on continuous cover forestry are complicated and typically incompatible with rotation models. This dichotomy is theoretically unsatisfactory and makes the choice between clearcuts and continuous cover forestry vague. We present a theoretically sound and empirically detailed generalized setup with an optimal clear-cut regime (or even-aged management) and optimal continuous cover regime (or uneven-aged management) as special cases. It includes a size-structured growth model, variable and fixed harvesting costs, and allows for the completely flexible optimization of harvest timing in both regimes. Flexible harvest timing becomes essential when optimizing the transition from clear-cut regimes toward continuous cover forestry. The model is applied to Norway spruce (Picea abies (L.) Karst.) and solved as a dynamic mixed-integer problem. Low or moderate site productivity, an interest rate above 2%, and a high artificial regeneration cost support the optimality of continuous cover forestry. In its most general form, the optimal clear-cut regime does not exist when the continuous cover regime is globally optimal, and when it exists, the rotation period lengthens with interest rate. The optimal choice between forest management regimes may depend on the initial stand state and whether the naturally regenerated seedlings are utilized in solutions with clearcuts. Maximizing sustainable yield favors clearcuts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".