Economics of harvesting boreal uneven-aged mixed-species forests
Bibliographic record
Abstract
The subject of this study is the economics of harvesting boreal uneven-aged mixed-species forests consisting of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), birch (Betula pendula Roth and B. pubescens Ehrh.), and other broadleaves. The analysis is based on an economic description of uneven-aged forestry, applying a size-structured model. The optimization problem is solved in its general dynamic form using gradient-based interior point methods. When volume yield is maximized, the optimal steady state is a nearly pure Norway spruce stand at all site types, producing slightly higher yields than single-species stands. After including sawlog and pulpwood prices, the net present value of stumpage revenues is maximized using 1%, 3%, and 5% interest rates and a 15-year harvesting interval. At less productive sites, the stands are nearly pure Norway spruce stands, regardless of the interest rate. At more productive sites, increasing the interest rate increases the species diversity, with optimal steady states consisting of both Norway spruce and birch. In some cases, rather small changes in relative prices change the optimal steady state into a birch-dominated stand. Optimal solutions converge to the same steady-state solutions, independent of the initial stand state. If other broadleaves without commercial value are not harvested, they will eventually dominate the stand.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".