Evaluation of forest management strategies based on Triad zoning
Bibliographic record
Abstract
Triad forest management was analyzed for a New Brunswick Crown License. Fifteen forest value indicators were used to describe social, economic, and environmental outcomes from forecast Triad scenarios, including 36 scenarios where reserves and intensively managed area varied in 5% increments from 10% to 35%. Some indicators were most sensitive to intensive area (e.g., silviculture cost), other to reserve area (e.g., area containing large snags), and still others to extensive area (e.g., average harvest levels). Some indicators averaged arithmetically, and could be kept constant if increases in reserves were accompanied by equal increases in intensive area. Such averaging for timber supply is often a selling point made by Triad advocates. Indeed, many different scenarios generated the same annual harvest when averaged over the 100-year forecast time horizon; however, immediate reductions in operable timber inventory resulting from reserve increases caused short-term harvest reductions, while future gains in yield from intensive area increases caused long-term harvest increases. This timing offset between losses and gains of operable volume, and its effect on harvest timing, may be impediments to Triad implementation in jurisdictions where timber supply is fully utilized. This analysis presents methods and results that may be of value to forest managers contemplating implementation of Triad zoning.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".