Possibilities for Refinement and Reduction: Future Improvements Within Regulatory Testing
Bibliographic record
Abstract
Approaches and challenges to refining and reducing animal use in regulatory testing are reviewed. Regulatory testing accounts for the majority of animals reported in the most painful and/or distressful categories in the United States and Canada. Refinements in testing, including the use of humane endpoints, are of increasing concern. Traditional approaches to reduction (e.g., improving experimental design) are being supplemented with complementary approaches, such as the use of tier testing to eliminate some chemicals prior to in vivo testing. Technological advances in telemetry and noninvasive techniques will help decrease either the demand for animals in testing or animal suffering. Further decreases in animal use will stem from international harmonization and coordination of testing programs. Progress in refinement and reduction faces a variety of broad challenges, including limited funding for research. In the specific area of refinement, a key challenge is the issue of distress (as distinct from pain). In the area of reduction, the practice of using unjustifiably high numbers of animals from small species (e.g., rodents) should be challenged. One case study of the use of carbon dioxide as a euthanasia agent illustrates the need for further analysis and research. Notwithstanding the complexities and challenges, the potential for refinement and reduction in regulatory testing is encouraging.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".