Accelerated nervous system development contributes to behavioral efficiency in the laboratory mouse: A behavioral review and theoretical proposal
Bibliographic record
Abstract
The emergence of the laboratory mouse as a favored species for genetic research has posed a number of problems for scientists interested in the reflection of genetic influences in mouse behavior. It is commonly thought that rat behavior, which has been studied more extensively than mouse behavior, could be easily generalized to mice. In this article, a number of categories of behavior displayed by the mouse (motor, spatial, defensive, social) are reviewed and contrasted with the same categories of behavior displayed by the rat. The comparison suggests that mouse behavior is simpler and more dependent upon elementary actions than the behavior of the rat. We suggest that the behavioral simplification in the mouse adapts it for a different ecological niche than that occupied by the rat. We propose that this simplification may be mediated by accelerated brain maturation during development. We further propose that this developmental acceleration in the mouse renders it less dependent upon complex social behavior and plastic nervous system changes associated with learning than the rat. This difference poses problems for the development of relevant methods of behavioral analysis and interpretation. Since the mouse's biological adaptations will be reflected in laboratory behavior, suggestions are made for behavioral approaches to the study and interpretation of mouse behavior.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".