Economically derived yields for even- and uneven-aged stands
Bibliographic record
Abstract
We propose an approach to develop economic-based yields for even- and uneven-aged stands that could be compared with yields generated by using silvicultural treatments. Economic-based yields are derived from economic parameters that describe markets and the landowner’s ownership goals and objectives. This study highlights five conclusions. First, economic-based yields define a lower bound on silvicultural-based yields required to just satisfy these economic parameters and provide a metric of confidence that a silvicultural prescription would increase (or decrease) the landowner’s wealth. Second, a main driver of the economic-based yields is the opportunity costs of the reserve growing stock or regeneration costs and the land. Third, the economic-based yields followed a similar pattern regardless of whether the stand was defined as even- or uneven-aged. Fourth, the economic-based yields illustrate the physical impacts that recreational leases, taxes, or the sale of nontimber forest ecosystem goods and services have on this lower bound. Finally, if the economic-based yields are greater than the silvicultural-based yields and if physical output estimates could be derived for the suite of nontimber forest ecosystem goods and services resulting from the forest structure, then implied economic values for this suite of goods and services could be derived using the models presented.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".