A replanning approach for maximizing woodland caribou habitat alongside timber production
Bibliographic record
Abstract
We present a forest harvest scheduling model that meets timber harvest targets while maximizing a proxy measure of woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) habitat based on the configuration of preferred habitat on the landscape. Woodland caribou within the boreal forest region in Canada tend to prefer mature jack pine forest stands, which tend to be rich in their preferred resource, lichen, and also reduce predation pressure. This can create conflict with industrial wood supply needs. We designed a model that can be used to identify good harvest scheduling plans given these competing objectives. Our approach is to use a series of sequential linear programming models that are solved within a replanning framework. Specifically, each individual linear programming model seeks to produce a solution that will meet timber harvest targets while minimizing the harvest of high-quality woodland caribou habitat stands. Stands are assessed with respect to their suitability as woodland caribou habitat based on their contribution to the overall landscape equivalent connected area (ECA), a combined spatial measure of preferred habitat amount and its connectivity. We used our model for a case study of the Trout Lake Forest in northwestern Ontario, Canada, and found that our model creates approximately 10% more caribou habitat than an earlier heuristic procedure and 30% more caribou habitat than the prevailing woodland caribou habitat forest management plan in the Trout Lake Forest while meeting the same timber harvest targets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".