Trapping and Marking Terrestrial Mammals for Research: Integrating Ethics, Performance Criteria, Techniques, and Common Sense
Bibliographic record
Abstract
We propose that researchers integrate ethics, performance criteria, techniques, and common sense when developing research trapping programs and in which members of institutional animal care and use committees address these topics when evaluating research protocols. To ask questions about ethics is in the best tradition of science, and researchers must be familiar with codes of ethics and guidelines for research published by professional societies. Researchers should always work to improve research methods and to decrease the effects on research animals, if for no other reason than to minimize the chances that the methods influence the animals' behavior in ways that affect research results. Traps used in research should meet performance criteria that address state-of-the-art trapping technology and that optimize animal welfare conditions within the context of the research. The proposal includes the following criteria for traps used in research: As Criterion I, killing-traps should render >/= 70% of animals caught irreversibly unconscious in </= 3 min (calculated with 95% confidence). As Criterion II, live-traps should trap >/= 70% of animals with </= 50 points scored for physical injury (calculated with 95% confidence). The types of traps described include killing-traps (snap traps, rotating jaw traps, snares, pitfalls, and drowning sets), common sets, and the common types of live-traps (box and cage traps, pitfalls, foothold traps. snares, corrals and nets, and dart collars). Also described are trapping methods for specific mammals, according to which traps fulfill Criteria I and II for which species, and techniques for short-term, long-term, and permanent marking of mammals.
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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 | 0.366 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.081 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 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".