A comparative analysis of management options for grizzly bear conservation in the U.S.–Canada trans-border area
Bibliographic record
Abstract
Abstract Grizzly bear (Ursus arctos) populations spanning the U.S.–Canada border in the south Selkirk, Purcell–Yaak, and Cabinet Mountains are small, vulnerable, and at the front lines of any further range contraction in North America. Recent genetics work demonstrated that the south Selkirk grizzlies are an isolated population (no male or female connectivity) of fewer than 100 individuals with a 15–20% reduction in genetic diversity and that the Purcell–Yaak population is declining and demographically isolated (no female connectivity) with fewer than 50 individuals. The <25 animals living in the Cabinet Mountains population are likely isolated from both the south Selkirk Mountain and the Purcell–Yaak populations. We recognize these populations need enhanced management. To guide the development of a comprehensive management plan, we explored the effects of 3 actions (population augmentation, enhanced population interchange, and reduced mortality through management actions). We simulated 2 populations of 50 and 100 individuals using population viability analysis (PVA) software (VORTEX). We examined these management actions and combinations of them on population growth rate and extinction probabilities. Our simulations suggest that augmentation had the largest demographic effect on population growth rate over the short-term, mortality reductions had the largest effect in the long-term, and establishing population interchange and reducing mortality had the greatest effect on extinction probability. Enhanced cooperative U.S. and Canadian efforts are required to address the issues facing these small grizzly populations and to build connectivity to existing larger populations and areas of vacant habitat. Our findings apply to recovery and conservation efforts for small populations of all species of bears.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".