Stability of inbred mouse strain differences in behavior and brain size between laboratories and across decades
Bibliographic record
Abstract
If we conduct the same experiment in two laboratories or repeat a classical study many years later, will we obtain the same results? Recent research with mice in neural and behavioral genetics yielded different results in different laboratories for certain phenotypes, and these findings suggested to some researchers that behavior may be too unstable for fine-scale genetic analysis. Here we expand the range of data on this question to additional laboratories and phenotypes, and, for the first time in this field, we formally compare recent data with experiments conducted 30-50 years ago. For ethanol preference and locomotor activity, strain differences have been highly stable over a period of 40-50 years, and most strain correlations are in the range of r = 0.85-0.98, as high as or higher than for brain weight. For anxiety-related behavior on the elevated plus maze, on the other hand, strain means often differ dramatically across laboratories or even when the same laboratory is moved to another site within a university. When a wide range of phenotypes is considered, no inbred strain appears to be exceptionally stable or labile across laboratories in any general sense, and there is no tendency to observe higher correlations among studies done more recently. Phenotypic drift over decades for most of the behaviors examined appears to be minimal.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".