Bibliographic record
Abstract
Sex has many costs with respect to asexual reproduction, so its ubiquity is a puzzle. There has been a continuing effort to identify general circumstances in which aspects of sex generate an evolutionary advantage over asexual reproduction. Here we focus on the generality that individuals can experience good and bad "luck" at various stages of their life history regardless of genotype, and on the interindividual nature of sex. Sexual outcrossing combines genetic information from individuals with potentially different experiences, so it is conceivable that sex might reduce the contribution of individual luck to noise in inheritance. In a simple way, we derive expressions for noise in inheritance in terms of some sources of within-generation ecological noise. We demonstrate that interindividual reproduction can indeed dampen the effects of ecological noise better than lone-individual modes, but there are conditions under which it does not. Empirical and theoretical work on plants, modeled here, suggest noise dampening conditions. Ecological noise dampening operates alongside other features of sex such as recombination and segregation and, because noise in inheritance weakens the role of selection in genetic change, we speculate that noise dampening may offer a benefit to be deducted from the costs of sex. We also suggest that the amount of selfing relative to outcrossing observed in natural populations may be influenced by the amount of individual-level ecological noise in a given habitat.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".