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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it