A multifunctional comparison of even-aged and uneven-aged forest management in a boreal region
Bibliographic record
Abstract
The choice between even- and uneven-aged forest management is a topical issue as the negative impacts of clear-felling are being increasingly criticized and the profitability of even-aged management has been questioned. This study compared these management systems in spruce and pine stands in terms of timber, carbon, and bilberry benefits, all of which can be predicted with reasonable accuracy and quantified in terms of money. Management was optimized by maximizing the total net present value (NPV) of the three benefits in a steady-state situation. The currently recommended type of even-aged management was also included in the comparisons. Uneven-aged management was the best in terms of the total NPV and with respect to bilberry benefits (NPV of bilberry harvesting). It was also better than even-aged management in terms of timber benefits when the discount rate was more than 1%. The ranking was less clear in terms of carbon sequestration and discounted carbon benefits.
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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 | 0.000 | 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 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".