On the economics of optimal timber production in boreal Scots pine stands
Bibliographic record
Abstract
This study analyzes the optimal management of Scots pine (Pinus sylvestris L.) stands by applying recent developments in numerical optimization methods and forest production ecology. Our approach integrates a process-based, stand-level growth model and a detailed economic description of stand management. The variables optimized include the initial stand density, the number, timing, type, and intensity of thinnings, and the rotation period. A generalized pattern search is used to maximize the present value of net timber revenue over an infinite time horizon. The model adopts quality pricing, which takes branch size and quality into account, to differentiate among five different timber assortments. The analysis also covers five different site types. The results demonstrate the necessity of optimizing all of the management variables simultaneously. Given a low interest rate, optimized thinning significantly increases the rotation period, volume yield, and economic outcome. At higher interest rates, optimal rotation may be shortest under the least fertile growth conditions. The inclusion of a detailed price structure reveals that previous results concerning sensitivity to timber price and the relationship between maximum sustainable yield and economic solutions do not hold true in models that provide a more realistic description of forest management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".