Assessing the trade-offs between timber supply and wildlife protection goals in boreal landscapes
Bibliographic record
Abstract
Protecting wildlife within areas of resource extraction often involves reducing habitat fragmentation. In Canada, protecting threatened woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) populations requires preserving large areas of intact forest habitat, with some restrictions on industrial forestry activities. We present a linear programming model that assesses the trade-off between achieving an objective of habitat protection for caribou populations while maintaining desired levels of harvest in forest landscapes. The habitat-protection objective maximizes the amount of connected habitat that is accessible by caribou, and the forestry objective maximizes net revenues from timber harvest subject to even harvest flow, a harvest target, and environmental sustainability constraints. We applied the model to explore the habitat protection and harvesting scenarios in the Cold Lake caribou range, a 6726 km2 area of prime caribou habitat in Alberta, Canada. We evaluated harvest scenarios ranging from 0.1 Mm3·year–1 to maximum sustainable harvest levels over 0.7 Mm3·year–1 and assessed the impact of habitat protection measures on timber supply costs. Protecting caribou habitat by deferring or reallocating harvest increases the timber unit cost by Can$1.1–2.0 m–3. However, this impact can be partially mediated by extending the harvest to areas of oil and gas extraction to offset forgone harvest in areas of prime caribou habitat.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".