Incorporating Laboratory Animal Science into Responsible Biomedical Research
Bibliographic record
Abstract
Biomedical research has made great strides in the past century leading to rapid advances in human life expectancy, all derived from improved understanding, prevention, and treatment of many diseases and conditions. Research involving laboratory animals has played a significant role in this medical progress. However, there continues to be controversy surrounding the use of animals in research, and animal models have been questioned regarding their relevance to human conditions. While research fraud and questionable research practices could potentially contribute to this problem, we argue that a relative ignorance of laboratory animal science has contributed to the "uncontrolled vivarium experiment" that runs parallel to the more controlled scientific experiment. Several variables are discussed, including husbandry, animal environment, social housing, and more, that can contribute to this uncontrolled experiment, and that can simultaneously decrease quality of life for rodent test subjects when ignored. An argument is put forward that laboratory animal veterinarians and scientists can and should play an important role in better controlling such variables. Similarly, the laboratory animal veterinarian and scientist should play an important role in responsible science by addressing complex interdisciplinary challenges.
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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 | 0.106 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| 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".