Killing Traps and Snares in North America: The Need for Stricter Checking Time Periods
Bibliographic record
Abstract
In this review, we make the point that current checking times for killing traps and snares are inadequate or nonexistent in most North American jurisdictions. We use Conibear 120 rotating-jaw traps and killing neck snares as examples of trapping devices that may fail to consistently and humanely kill furbearers. Because these killing devices are not powerful enough for the target species, the trigger systems do not properly position the animals in traps, or trappers are inexperienced and improperly set traps or snares, these killing devices become restraining devices, and animals suffer long and painful deaths. Because trappers use a variety of trigger configurations and trap sets, all killing devices, even those certified by trapper organizations or governments, should be monitored at least once every 24 h on traplines, but preferably every 12 h, because one cannot know a priori whether traps will strike animals in appropriate locations for a quick kill. However, when using trapping devices such as killing neck snares that are legal and allowed by government agencies despite being inhumane, trappers should check them every 12 h. When traplines are situated near urban areas, e.g., within 10 km, checks should be done every 12 h to release pets and non-target animals.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".