Triad forest management: Scenario analysis of forest zoning effects on timber and non-timber values in New Brunswick, Canada
Bibliographic record
Abstract
Triad forest management is a form of zoning under which land is allocated into extensively managed, intensively managed, and reserve zones, with management tailored in each zone such that all objectives are met collectively across the landbase. We evaluated the utility of triad management on the privately-owned, industrial, 190 000-ha Black Brook District in New Brunswick, Canada. Scenario planning was used to simulate effects of 64 allocation scenarios (0–15% reserve area, 39–64% intensively managed softwood, and 21–61% extensively managed) on forest species composition, age class distribution, timber growing stock, harvest levels, and old forest habitat. Softwood harvest in the short term (30 years) was insensitive to reserve and intensive management allocations but doubled in the long term as the intensive management zone was increased from 39% to 64%.Hardwood harvest was sensitive only to area in reserves, declining as the area allocated to reserves was increased. Abundance of old forest generally increased with the amount of reserve area, but varied by species composition. Management of this landbase is focused on timber production, and intensive management clearly provided major increases in timber yield. It also could permit setting aside additional reserve area; old mixedwood habitat in particular is in short supply. These were hypothetical scenarios, and the landowner is not necessarily pursuing any of modeled strategies. Although the range of desired values may differ on other landbases, a scenario analysis of alternative zoning is an effective means to select a management strategy. Key words: scenario planning, intensive forest management, reserves, extensive forest management, triad, land allocation, forest zoning
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".