Wolverine in the slipstream: A systematic review of caribou‐focused conservation benefits, gaps and uncertainties for wolverine in Canada
Bibliographic record
Abstract
Abstract With rates of biodiversity loss accelerating globally, debate exists regarding the most efficient ways of allocating resources to conserve species. Woodland caribou ( Rangifer tarandus caribou , hereafter “caribou”) are the focus of many Canadian conservation strategies. Here we examine the extent caribou‐focused conservation can benefit wolverine ( Gulo gulo ), a Species at Risk that overlaps with caribou in distribution and ecological aspects. We conducted a systematic review of Canadian conservation documentation (51 caribou, 14 wolverine documents) and North American scientific literature (550 caribou, 167 wolverine papers) to quantify for wolverine and caribou: (i) variation in conservation documentation availability and age, (ii) overlap in commonly listed threats and recovery actions, and (iii) the extent threats have been researched across Canada. While we found differences in conservation and research focus, both key threats (including habitat loss, hunting and trapping, sensory disturbance, and linear features) and recovery actions (including management of important habitat, partnerships, and population monitoring) were listed in >50% of conservation documentation for both wolverine and caribou. We identify caribou‐focused conservation actions that may support wolverine, and where gaps and uncertainties in wolverine management remain. Actions that effectively protect caribou critical habitat implicitly manage multiple threats relevant to wolverine.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".