Can a Hybrid Line Break a Selection Limit on Behavioral Evolution in Mice?
Bibliographic record
Abstract
Abstract Artificial selection yielded four replicate high runner (HR) lines of mice that reached apparent selection limits (~ threefold increase in wheel revolutions per day vs. four control lines), despite maintenance of additive genetic variance. After 68 generations, we used animal models to test for changes in additive-genetic variances and covariance of the two measured components (average speed and duration) of running distance. We also attempted to break the selection limit by crossing two HR lines, then continuing directional selection on this hybrid line and on the two parental lines for nine generations. The genetic correlation between speed and duration was positive in the base population, but evolved to be negative in the two parental HR lines. Although heritability for both speed and duration (but not distance) increased in the hybrid line, their genetic correlation remained negative. Hybrid F1 mice from generation 68 parents showed heterosis for running distance, which was lost in subsequent generations, and the hybrid line did not exceed the limit. Both male and female hybrids ran faster than parental lines for most generations, but running duration was intermediate or reduced, reflecting their negative genetic correlation. The evolved genetic trade-off between speed and duration may explain the inability for the hybrid line to break the selection limit for distance run, despite renewed additive genetic variance for at least one of its component traits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".