The Use of Genetically-engineered Animals in Science: Perspectives of Canadian Animal Care Committee Members
Bibliographic record
Abstract
The genetic engineering of animals for their use in science challenges the implementation of refinement and reduction in several areas, including the invasiveness of the procedures involved, unanticipated welfare concerns, and the numbers of animals required. Additionally, the creation of genetically-engineered animals raises problems with the Canadian system of reporting animal numbers per Category of Invasiveness, as well as raising issues of whether ethical limits can, or should, be placed on genetic engineering. A workshop was held with the aim of bringing together Canadian animal care committee members to discuss these issues, to reflect on progress that has been made in addressing them, and to propose ways of overcoming any challenges. Although previous literature has made recommendations with regard to refinement and reduction when creating new genetically-engineered animals, the perception of the workshop participants was that some key opportunities are being missed. The participants identified the main roadblocks to the implementation of refinement and reduction alternatives as confidentiality, cost and competition. If the scientific community is to make progress concerning the implementation of refinement and reduction, particularly in the creation and use of genetically-engineered animals, addressing these roadblocks needs to be a priority.
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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 | 0.092 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.057 | 0.031 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 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".