Synthesis of habitat models for management of wolverine (Gulo gulo): Identifying key habitat and snow refugia in the Columbia and Rocky Mountains, Canada
Bibliographic record
Abstract
The wolverine is a wide-ranging and frequently at-risk or data-deficient species with dramatic range contractions across the northern hemisphere. Recent reports of low population densities inside and outside protected areas in western North America highlight the need for better conservation practice, policy, and planning across large landscapes. To assess broad habitat needs, we synthesized available wolverine habitat models in the Columbia and Rocky Mountains (63,000 km2), Canada. We used coefficients from four existing models to create spatial predictions over environmental datasets including snow, landcover, and roads. We averaged predictions using the distance-weighted mean of equal-area percentile habitat values and validated the output by comparison to independent data. Because persistent spring snow is tightly correlated with high-quality habitat, we assessed 2080 spring snow cover forecasts (under Representative Concentration Pathway (RCP) 8.5 high emissions scenario) to identify potential habitat refugia in British Columbia. Our synthesized habitat model identified high-quality habitat along mountain ranges, notably in the Purcell Mountains and the Columbia Icefield in the Rocky Mountains. Mean habitat value was 0.70 (SD: 0.19) inside protected areas and 0.55 (SD: 0.28) outside protected areas. The British Columbia side of the study area is forecasted to lose 44% of persistent spring snow cover by 2080, with declines identified inside many protected areas. By synthesizing existing habitat research and climate forecasts, we provided new insights at the broad spatial scale needed to conserve wide-ranging species like wolverine and to inform land-use planning for recreational access and the establishment and management of protected and conserved areas.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".