Both Male-Biased and Female-Biased Genes Evolve Faster in Fish Genomes
Bibliographic record
Abstract
Males and females often display extensive phenotypic differences, and many of these sexual dimorphisms are thought to result from differences between males and females in expression of genes present in both sexes. Sex-biased genes have been shown to exhibit accelerated rates of evolution in a wide array of species, however the cause of this remains enigmatic. In this study, we investigate the extent and evolutionary dynamics of sex-biased gene expression in zebrafish. Our results indicate that both male-biased genes and female-biased genes exhibit accelerated evolution at the protein level. In order to differentiate between adaptive and nonadaptive causes, we tested for codon usage bias and signatures of different selective regimes in our sequence data. Our results show that both male- and female-biased genes show signatures consistent with adaptive evolution. In order to test the generality of our findings across fish, we also analyzed publicly available data on sticklebacks, and found results consistent with our findings in zebrafish.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".