Assessing forest management scenarios on an Aboriginal territory through simulation modeling
Bibliographic record
Abstract
The dominant management strategy in boreal forests—aggregated clearcuts (AC)—faces increased criticism by various stakeholders, including Aboriginal people. Two alternative strategies have been proposed: dispersed clearcuts (DC) and ecosystem-based management (EM). We modelled the long-term and landscape-scale effects of AC, DC, and EM on a set of indicators of sustainable forest management relevant to an Aboriginal community's values: (1) forest age structure; (2) spatial configuration of forest stands; (3) road network density; and, (4) forest habitat loss to clearcuts. EM created a forest age structure closer to what would result from a natural disturbance regime, compared to AC and DC. Cut blocks were more evenly distributed with EM and DC. The road network density was lower and increased slower with EM, thus reducing the potential for conflicts between forest users. Under EM, a higher forest cover was maintained (and thus potential wildlife habitat) than in AC or DC. The EM scenario provided the best outcome based on the four measured indicators, partly because the constraints imposed on the modeling exercise led it to harvest less than the other scenarios. Annual allowable cut should thus be a key factor to consider to ensuring better compliance with Aboriginal criteria of sustainable forest management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".