Multiple sex chromosomes in teleost fishes from a cytogenetic perspective: state of the art and future challenges
Bibliographic record
Abstract
Despite decades of cytogenetic and genomic research of dynamic sex chromosome evolution in teleost fishes, multiple sex chromosomes have been largely neglected. In this review, we compiled available data on teleost multiple sex chromosomes, identified major trends in their evolution and suggest further trajectories in their investigation. In a compiled dataset of 440 verified records of fish sex chromosomes, we counted 75 multiple sex chromosome systems with 60 estimated independent origins. We showed that male-heterogametic systems created by Y-autosome fusion predominate and that multiple sex chromosomes are over-represented in the order Perciformes. We documented a striking difference in patterns of differentiation of sex chromosomes between male and female heterogamety and hypothesize that faster W sex chromosome differentiation may constrain sex chromosome turnover in female-heterogametic systems. We also found no significant association between the mechanism of multiple sex chromosome formation and percentage of uni-armed chromosomes in teleost karyotypes. Last but not least, we hypothesized that interaction between fish populations, which differ in their sex chromosomes, can drive the evolution of multiple sex chromosomes in fishes. This underlines the importance of broader inter-population sampling in studies of fish sex chromosomes. This article is part of the theme issue 'Challenging the paradigm in sex chromosome evolution: empirical and theoretical insights with a focus on vertebrates (Part II)'.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".