Assessing the statistical power of genetic analyses to detect multiple mating in fishes
Bibliographic record
Abstract
A single‐sex model is presented that calculates the probability of detecting multiple mating (PrDM) given genetic data from the single genetic parent and a sample of its offspring. The model incorporates the effects of numbers of loci, alleles, offspring and genetic parents contributing to the multiple mating, all of which effect PrDM. The model is used to determine the actual number of loci and offspring that are required to detect multiply mated broods with high probability (80 and 95%). For example, if two sires contribute with equal fertilization success to multiply mated broods, then only 10 offspring and one locus with seven equally common alleles are required to ensure that 80% of multiple mated broods are detected. Ninety‐five per cent of multiple mated broods can be detected with 10 offspring and five loci with four equally common alleles. The utility of the model is demonstrated with biological examples addressing geographic variation in multiple paternity among natural populations of guppies Poecilia reticulata and mosquitofish Gambusia holbrooki.
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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 | 0.091 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".