Bibliographic record
Abstract
Optimal harvesting of Norway spruce ( Picea abies (L.) Karst.) forests is studied applying an individual-tree model for uneven-aged management. Optimization is carried out by gradient-based, large-scale interior point methods. Assuming volume maximization and natural regeneration, it is optimal to apply uneven-aged management. Under artificial regeneration, the result is the reverse. Economically optimal solutions with a 20-year harvesting interval produce an annual sawn timber output of 4.4–2.4 m 3 ·ha –1 depending on thermal zone and interest rate. Before harvest basal area varies between 18 and 12 m 2 ·ha –1 and the diameter of harvested trees between 15 and 33 cm. In contrast with the classic inverted J-structure, optimal steady-state size structure resembles a serrate form. Profitability of even- and uneven-aged management is compared assuming that the initial stand state represents an optimal uneven-aged steady state. A switch to even-aged management is optimal given the most favorable growth conditions and interest rate below 1%–2%. In other cases, it is economically optimal to continue uneven-aged management although volume output remains lower than under even-aged management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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 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".