Altered movement patterns in wolverines with missing paws
Bibliographic record
Abstract
• Wolverines need to move large distances to carry out life-history activities, thus movement is related to their fitness. • Injuries can significantly impact animal fitness. • Injured wolverines showed reduced daily movement rates. • Inured wolverines were hit by vehicles on roads. • Wolverines are susceptible to bycatch and incidental harvest. Injury may affect an animal’s ability to move and carry out life history activities, ultimately affecting their fitness. During a larger telemetry study in northwestern Ontario, Canada, we live-captured 2 injured male wolverines who were missing their front right paw. We used GPS collars to compare their daily movements with temporally aligned movements from 27 uninjured male wolverines. Injured males traveled less distance, used smaller areas, moved along more sinuous paths, lived closer to towns, and were in the lowest quantile of body mass. We predicted injured males would move less in snow due to increased sinking depth, but found they moved less in snow-free months. One of the injured males made a > 188 km exploratory movement in 9 days and both individuals lived for at least 2 years after we detected their injury. Ultimately, both were killed by vehicles on provincial highways. We present the first detailed examples of the movement characteristics of injured wolverines. Our results suggest that injured wolverines can fulfill some life history activities such as dispersal and short-term survival which are factors in population demography, but that permanent injury has significant effects on wolverine movement and may be a hidden population level effect. Our data provide information on the sub-lethal effects of injury on movement and contribute to the understanding of the effects of human activities on 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".