Data from: Sexually discordant selection is associated with trait specific morphological changes and a complex genomic response
Bibliographic record
Abstract
Sexes often have differing fitness optima, potentially generating intra-locus sexual conflict, as each sex bears a genetic ‘load’ of alleles beneficial to the other sex. One strategy to evaluate conflict in the genome is to artificially select populations discordantly, against established sexual dimorphism, reintroducing attenuated conflict. We investigate a long-term artificial selection experiment reversing sexual size dimorphism in Drosophila melanogaster during ~350 generations of sexually discordant selection. We explore morphological and genomic changes to identify loci under selection between the sexes in discordantly and concordantly size selected treatments. Despite substantial changes to overall size, concordant selection maintained ancestral sexual dimorphism. However, discordant selection altered size dimorphism in a trait-specific manner. We observe multiple, possible soft selective sweeps in the genome, with size related genes showing signs of selection. Patterns of genomic differentiation between the sexes within lineages identified potential sites maintained by sexual conflict. One discordant selected lineage shows a pattern of elevated genomic differentiation between males and females, on chromosome 3L, consistent with the maintenance of sexual conflict. Our results suggest visible signs of conflict and differentially segregating alleles between the sexes due to discordant selection.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.025 |
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".