Considerations for furbearer trapping regulations to prevent grizzly bear toe amputation and injury
Bibliographic record
Abstract
Abstract Under the North American Model of wildlife Conservation, wildlife managers are encouraged to update management approaches when new information arises whose implementation could improve the viability of wildlife populations and the well‐being of animals. Here we detail an observation of several grizzly bears with amputated toes in southeast British Columbia between 2016‐2020 and assemble evidence to inform management strategies to remedy the issue. During the capture of 59 grizzly bears, 4 individuals (~7%) had amputated toes on one of their front feet. The wounds were all healed and linear in nature. Further opportunistic record collection revealed that similar examples of amputated toes occurred beyond our study area in neighboring mountain ranges and internationally. We found evidence that seasonal overlap between the active season for grizzly bears and the fall trapping seasons—for small furbearers with body‐gripping traps and for wolves with foothold traps—were frequently responsible for toe amputation. Photo evidence suggested that body‐gripping traps were the main trap type associated with toe amputation. Multiple options to reduce or eliminate the incidental amputation of grizzly bear toes exist, and the options have varying degrees of expected efficacy and require differing levels of monitoring. One option is to delay the start of the marten trapping season until December 1, when most bears have denned, instead of opening the season on or prior to November 1, when more than 50% of bears are still active. An alternative solution, such as a license condition that requests trappers narrow trap‐box entrances to exclude bear feet while still allowing entrance of target furbearers, has the potential to minimize accidental capture of bears, but the effectiveness of this approach is unknown. Finally, experimental toe extraction trials suggested that better anchoring traps was not a viable solution given that adult grizzly bear feet only came free from body‐gripping traps 20% of the time under maximum human force. At least 230 kg pull was required to consistently free toes from body‐gripping traps, which not all bears will be able to produce unless the cable is long and allows a running start. Solutions that do not involve season changes will require monitoring of efficacy and compliance to ensure success.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".