Guidelines development and scientific uncertainty: use of previous case studies to promote efficient production of guidelines on the care and use of fish in research, teaching and testing
Bibliographic record
Abstract
Abstract The Canadian Council on Animal Care (CCAC) develops guidelines on issues of current and emerging concern in response to the needs of the scientific community, advances in animal care, and the needs of the CCAC Assessment Program. Guidelines are developed by subcommittees of experts, and are based on sound scientific evidence. However, the process of guidelines' development can involve consideration of areas where there is little scientific certainty or where scientific evidence needs to be tempered by other ethical considerations. Often these are areas where recommendations to the community are most needed, to provide assistance to both investigators and animal care committees on how best to balance the well-being of experimental animals and the goals of scientific research. The process for drafting the CCAC guidelines on: the care and use of fish in research, teaching and testing (in preparation) will be used as an example of the development of guidelines in the face of uncertain science, alongside a discussion of the CCAC guidelines on: transgenic animals (1997), as an example of the employment of a precautionary approach. Fish are now one of the most commonly used laboratory animals in Canada. However, what constitutes well-being for fish is an emerging field with often conflicting scientific data, and this presents unique challenges in guidelines' development.
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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 | 0.169 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".