The evolution of sex chromosomes in organisms with separate haploid sexes
Bibliographic record
Abstract
The evolution of dimorphic sex chromosomes is driven largely by the evolution of reduced recombination and the subsequent accumulation of deleterious mutations. Although these processes are increasingly well understood in diploid organisms, the evolution of dimorphic sex chromosomes in haploid organisms (U/V) has been virtually unstudied theoretically. We analyze a model to investigate the evolution of linkage between fitness loci and the sex-determining region in U/V species. In a second step, we test how prone nonrecombining regions are to degeneration due to accumulation of deleterious mutations. Our modeling predicts that the decay of recombination on the sex chromosomes and the addition of strata via fusions will be just as much a part of the evolution of haploid sex chromosomes as in diploid sex chromosome systems. Reduced recombination is broadly favored, as long as there is some fitness difference between haploid males and females. The degeneration of the sex-determining region due to the accumulation of deleterious mutations is expected to be slower in haploid organisms because of the absence of masking. Nevertheless, balancing selection often drives greater differentiation between the U/V sex chromosomes than in X/Y and Z/W systems. We summarize empirical evidence for haploid sex chromosome evolution and discuss our predictions in light of these findings.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".