RESEARCH ARTICLE Prioritizing Sites for Protection and Restoration for Grizzly Bears (Ursus arctos) in
Bibliographic record
Abstract
As the influence of human activities on natural systems continues to expand, there is a growing need to prioritize not only pristine sites for protection, but also degraded sites for restoration. We present an approach for simultaneously prioritizing sites for protection and restoration that considers landscape patterns for a threatened population of grizzly bears (Ursus arctos) in southwestern Alberta, Canada. We considered tradeoffs between bottom-up (food resource supply) and top-down (mortality risk from roads) factors affecting sea-sonal habitat quality for bears. Simulated annealing was used to prioritize source-like sites (high habitat productivity, low mortality risk) for protection, as well as sink-like sites (high habitat productivity, high mortality risk) for restoration. Priority source-like habitats identified key conservation areas where future developments should be limited, whereas priority sink-like habitats identified key areas for mitigating road-related mortality risk with access man-agement. Systematic conservation planning methods can be used to complement tradi-tional habitat-based methods for individual focal species by identifying habitats where conservation actions (both protection and restoration) have the highest potential utility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".