The dark phase improves genetic discrimination for some high throughput mouse behavioral phenotyping
Bibliographic record
Abstract
Dark-phase testing has previously been shown by others to improve the outcome of some 'classical' behavior test situations. However, the importance of such ethological correctness and the effect of the light/dark cycle on high throughput behavioral testing situations such as 'mutant vs. wild type' and 'screening', are less or unknown, respectively. These testing situations differ from the 'classical' in that they are designed primarily to discriminate between genetically different mice rather than provide a detailed assessment of ability or psychosocial state. Here we test the hypotheses that dark-phase testing affects the outcome of high throughput behavioral tests and that dark-phase testing improves discrimination between genetically distinct mice (C57BL/6J, 129S1/SvImJ and B6129F1) using high throughput behavioral tests. Our results demonstrate that, although all successful tests showed some effect of phase, only the SHIRPA primary screen, open-field test and motor learning on the rotarod showed improved strain discrimination in the dark phase. Surprisingly, the social interaction test did not show a clear benefit to either phase, and interestingly, the tail-flick test discriminated strains better in the light phase. However, since the preponderance of our data shows that dark-phase testing improves, or does not affect, strain discrimination, we conclude that for these strains and tests, dark-phase testing provided superior outcomes. If discrimination is not achieved in the dark phase, then light phase-testing would be undertaken.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".