Expanding the Three Rs to Meet New Challenges in Humane Animal Experimentation
Bibliographic record
Abstract
The Three Rs are the main principles used by Animal Ethics Committees in the governance of animal experimentation, but they appear not to cover some ethical issues that arise today. These include: a) claims that certain species should be exempted on principle from harmful research; b) increased emphasis on enhancing quality of life of research animals; c) research involving genetically modified (GM) animals; and d) animals bred as models of disease. In some cases, the Three Rs can be extended to cover these developments. The burgeoning use of GM animals in science calls for new forms of reduction through improved genetic modification technology, plus continued attention to alternative approaches and cost-benefit analyses that include the large numbers of animals involved indirectly. The adoption of more expanded definitions of refinement that go beyond minimising distress will capture concerns for enhancing the quality of life of animals through improved husbandry and handling. Targeting refinement to the unpredictable effects of gene modification may be difficult; in these cases, careful attention to monitoring and endpoints are the obvious options. Refinement can also include sharing data about the welfare impacts of gene modifications, and modelling earlier stages of disease, in order to reduce the potential suffering caused to disease models. Other issues may require a move beyond the Three Rs. Certain levels of harm, or numbers and use of certain species, may be unacceptable, regardless of potential benefits. This can be addressed by supplementing the utilitarian basis of the Three Rs with principles based on deontological and relational ethics. The Three Rs remain very useful, but they require thoughtful interpretation and expansion in order for Animal Ethics Committees to address the full range of issues in animal-based research.
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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 | 0.261 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.015 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".