The Effect of Nonindependent Mate Pairing on the Effective Population Size
Bibliographic record
Abstract
The effective population size (N(e)) quantifies the effectiveness of genetic drift in finite populations. When generations overlap, theoretical expectations for N(e) typically assume that the sampling of offspring genotypes from a given individual is independent among successive breeding events, even though this is not true in many species, including humans. To explore the effects on N(e) of nonindependent mate pairing across breeding events, we simulated the genetic drift of mitochondrial DNA, autosomal DNA, and sex chromosome DNA under three mating systems. Nonindependent mate pairing across breeding seasons has no effect when all adults mate pair for life, a small or moderate effect when females reuse stored sperm, and a large effect when there is intense male-male competition for reproduction in a harem social system. If adult females reproduce at a constant rate irrespective of the type of mate pairing, the general effect of nonindependent mate pairing is to decrease N(e) for paternally inherited components of the genome. These findings have significant implications for the relative N(e) values of different genomic regions, and hence for the expected levels of DNA sequence diversity in these regions.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".