Where to begin? A flexible framework to prioritize caribou habitat restoration
Bibliographic record
Abstract
Habitat loss is a leading threat to many species at risk, and as such, the need for habitat restoration is widespread. In the boreal forests of Western Canada, habitat restoration is a key habitat management action needed to achieve self‐sustaining populations of woodland caribou, a federally threatened species in decline. Hundreds of thousands of kilometers of linear features were created during the exploration or extraction of oil and gas that are no longer used, yet natural regeneration remains stagnated. Only a fraction of these linear features is restored each year, sparking the need for managers to prioritize efforts. We developed an algorithm to prioritize habitat restoration and demonstrate how it can be used to predict and monitor progress towards restoration goals. Our approach is based on the idea of maximizing the gain in unaltered caribou habitat per unit cost, while allowing for the inclusion of different goals, costs, and weighting criteria. Our algorithm ranked landscape units into five zones of restoration priority. The largest gain in unaltered habitat occurred following restoration of the highest priority zones, with diminishing returns as restoration proceeded. None of the caribou ranges reached habitat management targets when not considering restoration within energy project boundaries, even after all candidate linear features were restored. Our results highlight the need for ambitious, coordinated restoration, and the need for improved land‐use planning to minimize alteration within caribou range. We demonstrate the flexibility of our algorithm by applying the framework to a case study in a mountain ecosystem.
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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 | 0.001 |
| 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) | 0.001 | 0.014 |
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".