A multifunctional comparison of even-aged and uneven-aged forest management in a boreal region
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 it