Selection of Reserves for Woodland Caribou Using an Optimization Approach
Bibliographic record
Abstract
Habitat protection has been identified as an important strategy for the conservation of woodland caribou (Rangifer tarandus). However, because of the economic opportunity costs associated with protection it is unlikely that all caribou ranges can be protected in their entirety. We used an optimization approach to identify reserve designs for caribou in Alberta, Canada, across a range of potential protection targets. Our designs minimized costs as well as three demographic risk factors: current industrial footprint, presence of white-tailed deer (Odocoileus virginianus), and climate change. We found that, using optimization, 60% of current caribou range can be protected (including 17% in existing parks) while maintaining access to over 98% of the value of resources on public lands. The trade-off between minimizing cost and minimizing demographic risk factors was minimal because the spatial distributions of cost and risk were similar. The prospects for protection are much reduced if protection is directed towards the herds that are most at risk of near-term extirpation.
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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 | 0.000 |
| 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".