Low wolverine (<i>Gulo gulo</i>) density in a national park complex of the Canadian Rocky Mountains
Bibliographic record
Abstract
Large carnivores are sensitive to human-caused extirpation due to large home ranges, low population densities, and low reproductive rates. Protected areas help maintain populations by acting as sources, but human-caused mortality, habitat displacement, and edge effects occurring at protected area boundaries may reduce that function. The national parks Banff, Yoho, and Kootenay in the Canadian Rocky Mountains are refugia for large carnivores, including wolverines (Gulo gulo (Linnaeus, 1758)). Despite growing conservation concern, empirical baseline population data for wolverines remain scarce throughout their range, including most of Canada. We hypothesized (i) that in these national parks, wolverine density matched values expected for high-quality habitat, and (ii) that edge effects decreased density towards park boundaries. We conducted systematic non-invasive genetic sampling surveys covering >7000 km2 (2011 and 2013). Using spatial capture–recapture models, we estimated mean (±SE) female (1.5 ± 0.3 and 1.4 ± 0.3 wolverine/1000 km2), male (1.8 ± 0.4 and 1.5 ± 0.3 wolverine/1000 km2), and combined (3.3 ± 0.5 and 3.0 ± 0.4 wolverine/1000 km2) densities for 2011 and 2013, respectively. These estimates were lower than predictions based on density extrapolation from nearby high-quality habitat, and density decreased towards park boundaries. To benefit the population, we recommend creating buffer zones around parks that protect female habitat and prohibit harvest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".