Rating of killing traps against humane trapping standards using computer simulations
Bibliographic record
Abstract
The Agreement on International Humane Trapping Standards (AIHTS) which applies to wildlife management, vertebrate pest control, and trapping for fur, skin, or meat for 19 listed species requires that a trapping method render at least 80% of a minimum of 12 target animals irreversibly insensible within a species-specific time limit. However, the Agreement also allows for the use of other scientifically proven methods as a substitute for testing on live animals. For the past five years, we have been developing computer models and simulation systems to determine whether killing traps meet humane trapping standards. The models were designed to classify the time-to-loss-of-sensibility of furbearing species based on mechanical characteristics of traps and strike location(s). Models were based on data collected from trap testing on marten (Martes americana), fisher (Martes pennanti), and raccoon (Procyon lotor). Models were tested against 15 years of live trap testing data from the Fur Institute of Canada. The models proved to be a valid alternative to trap testing on live animals due to their high levels of safe prediction accuracy (88%, 86% , and 92% for marten, fisher, and raccoon, respectively). If applied to trap testing, these models would dramatically reduce the cost and the need for trap testing on live 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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".