‘I think it is the toughest animal in the North’: human-wolverine interactions among hunters and trappers in the Canadian Northwest Territories
Bibliographic record
Abstract
The wolverine (Gulo gulo), a carnivore species of ‘Special Concern’ for its western population and ‘Endangered’ for its eastern population, is of special management concern in Canada. Hence understanding human-wolverine relationships and human perceptions toward this carnivore species has become important. Moreover, wolverines are harvested for fur in northern Canada, thus hunters and trappers who live in the vicinity with this species are key stakeholders. Using semi-structured interviews and questionnaires we analysed human-wolverine interactions and perceptions among Dene and Métis hunters and trappers in the Canadian Northwest Territories. We found that hunters and trappers had comprehensive knowledge about wolverine ecology and behavior. Values associated with this species ranged from respect for their tenacious character and strength, to describing the wolverine as a trickster. Stories emphasizing the wolverines’ mischievous nature were also common. Dene and Métis hunters and trappers acknowledge the importance of the wolverine in the socio-ecological system and have observed the cumulative impacts that climate and human-induced landscape change have had on wolverine habitat and population dynamics. Listening to hunters and trappers is one path towards more insightful management options in situations involving conflicts with wolverines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".