Incorporating local ecological knowledge to explore wolverine distribution in Alberta, Canada
Bibliographic record
Abstract
ABSTRACT Wolverines ( Gulo gulo ) occur at low densities in remote areas that are typically difficult to access, which has resulted in a lack of baseline data and uncertain status across parts of their range. We surveyed trappers in 2012 to gather information on local ecological knowledge of wolverine occurrence across a range of latitudes (49–59°N) in Alberta, Canada. We received questionnaires from 164 trapping areas in the Boreal Forest, Foothills, and Rocky Mountains. Similar to results from other methods of data collection, trapper observations of wolverines were associated with cooler climates and less anthropogenic disturbance. When we included data from all regions, the best model that explained recent wolverine observations included percent intact forest within the surrounding area. The odds ratio suggested that each increase of 1% in the amount of intact forest increased the odds of a trapper observing wolverine sign by 4%. In the Boreal Forest, the top model indicated that wolverines were more likely to be found in areas that had a cooler climate and more intact forest. Insights from trappers provided valuable baseline data on a sensitive species that is complementary to other research findings, and stimulated hypotheses that wolverines are linked to cooler climates and less disturbed environments. © 2019 Alberta Conservation Association. Wildlife Society Bulletin Published by Wiley Periodicals, Inc on behalf of The Wildlife Society.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".