Waking inactivity as a welfare indicator in laboratory mice: investigating postures, facial expressions and depression-like states
Bibliographic record
Abstract
Animal welfare assessment relies on valid and practical indicators of affect. In mice, the most widely used research vertebrates, lying still with eyes open, inactive-but-awake (IBA) in the home cage, has potential to be one such indicator. IBA is elevated in barren, conventional housing compared with well-resourced, enriched housing, and predicts immobility in Forced Swim Tests, a common measure of 'helplessness' in depression research. In Experiment 1, using females from three strains (C57BL/6, Balb/c and DBA/2), we first replicated past findings, confirming higher levels of IBA in conventional cages and a positive relationship between IBA and helplessness. We then extended this research to three other signs of depression: changes in weight and sleep, and reduced hippocampal volume. Here, IBA positively covaried with body mass index, with sleep in DBA/2s and conventionally housed BALB/cs, and negatively covaried with hippocampal volume in conventionally housed C57BL/6s. In Experiment 2, we sought to refine the phenotype of IBA to improve its accuracy as a welfare indicator. Here, scoring IBA performed in hunched postures appeared to improve its accuracy as an indicator in Balb/c mice. Additional research is now needed to further refine the phenotype of IBA and to confirm whether it reflects states consistent with depression, or instead other underlying poor welfare conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".