The Abundance and Distribution of Wolverines in British Columbia, Canada
Bibliographic record
Abstract
ABSTRACT The abundance and distribution of carnivores and their habitat are key information needed for status assessment, conservation planning, population management, and assessment of the effects of human development on their habitat and populations. We developed a habitat quality rating system, using existing wolverine ( Gulo gulo ) distribution, wolverine food, ecosystem mapping, and human development data. We used this and empirically derived estimates of wolverine density to predict wolverine distribution and abundance at a provincial scale. Density estimates for wolverines in high‐quality habitat averaged 6.2 wolverines/1,000 km 2 (95% CI = 4.2–9.5). We predicted mean densities ranging from 0.3/1,000 km 2 in rare‐quality habitat to 4.1/1,000 km 2 in moderate‐quality habitat. Our predicted population estimate for wolverines in British Columbia was 3,530 (95% CI = 2,700‐4,760). We predicted highest densities of wolverines in interior mountainous regions, moderate densities in interior plateau and boreal forest regions, and low densities in mainland coastal regions and drier interior plateaus. We predicted that wolverines would be rare on Vancouver Island, along the outer mainland coast, and in the dry interior forests, and absent from the Queen Charlotte Islands, interior grassland environments, and areas of intensive urban development.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".