Optimizing continuous cover and rotation forestry in mixed-species boreal forests
Bibliographic record
Abstract
We study the management of mixed-species boreal forests and tree species composition in a stand-level economic–ecological, size-structured model. The model includes ecological tree species interaction, a detailed harvesting cost module, optimal harvest timing, and optimization between continuous cover and rotation forestry. Optimization is solved by applying a trilevel structure in which the optimal rotation is the highest-level problem, harvest timing is the mid-level problem, and thinning intensity is the lowest-level problem. Given realistic regeneration costs and a 3% interest rate, continuous cover forestry is optimal and may include up to a 40% fraction of broadleaves. A low interest rate and low regeneration cost together with the presence of light-demanding Scots pine (Pinus sylvestris L.) favours rotation forestry. Eurasian aspen (Populus tremula L.) decreases the bare-land value but is optimal to fell without utilization only when it has no commercial value. Overyielding in terms of cubic metre output does not reveal the economically preferable species combination. Managing single-species stands by removing other species that are naturally regenerated decreases the economic outcome by 35%–44%. Felling noncommercial trees without utilization shows that the economically optimal solution avoids high-grading. Maintaining the number of large-diameter trees beyond the level that maximizes profitability implies only minor losses. Omitting thinning decreases the bare-land value up to 73%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".