Finite Populations, Finite Resources, and the Evolutionary Maintenance of Genetic Recombination
Bibliographic record
Abstract
Most previous models for the evolution of sex implicitly assume infinite population sizes and limitless resources. However, because favorable mutations are very rare and eukaryotic populations are finite, it has already been shown that multiple favorable mutants virtually never occur by chance. Therefore, sex is required to combine different favorable mutations into a single lineage. Second, we show that even when multiple favorable mutations do coexist, competition between genotypes can create negative epistasis for fitness, thus favoring recombination. Competition is especially effective when selection is at the level of viability in K-selected species living in a resource-limited environment. This means that recombination is advantageous both for incorporating new favorable mutations into the gene pool and for accelerating their increase to fixation. These advantages of recombination are diminished, however, as genome sizes decrease or as the amount of competition within the species is a less important component of selection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".