Shall we dance or shall we fight? Using DNA sequence data to untangle controversies surrounding sexual selection
Bibliographic record
Abstract
Population and evolutionary genetics studies have largely benefitted from advances in DNA manipulation and sequencing, as well as DNA data analysis techniques. Molecular evolution studies of male reproductive genes show a pattern of rapid evolution shaped, in some cases, by an adaptive selective process. Despite the large body of data on male reproductive genes, the female side of the story has remained unexplored. The few cases of female egg receptors analyzed also show rapid evolution. However, to disentangle between competing hypotheses on how selection operates on male x female molecular interaction leading to fertilization, we need to find male and female molecules that are partners in fertilization. A conflict model of sexual selection (similar to a host-parasite model) assumes a male-driven system where females are being forced under suboptimal conditions. This predicts that the amount of divergence at a female receptor depends on the amount of divergence among the male reproductive proteins that it binds (i.e., males are leading). Under a classical model of runaway sexual selection, female protein receptors might be the key to the rapid molecular changes observed in male reproductive proteins and higher divergence should be expected among female receptors than among their respective male binding proteins.
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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 | 0.002 |
| 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 | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".