A Bayesian Model for Assessing the Frequency of Multiple Mating in Nature
Bibliographic record
Abstract
Many breeding systems have multiple mating, in which males or females mate with multiple partners. With the advent of molecular markers, it is now possible to detect multiple mating in nature. However, no model yet exists to effectively assess the frequency of multiple mating (f(mm))--the proportion of broods with at least two males (or females) genetically contributing--from limited genetic data. We present a single-sex model based on Bayes' rule that incorporates the numbers of loci, alleles, offspring, and genetic parents. Two genetic criteria for calculating f(mm) are considered: the proportion of broods with three or more paternal (or maternal) alleles at any one locus and the total number of haplotypes observed in each brood. The former criterion provides the most precise estimates of f(mm). The model enables the calculation of confidence intervals and allows mutations (or typing errors) to be incorporated into the calculation. Failure to account for mutations can result in overestimates of f(mm). The model can also utilize other biological data, such as behavioral observations during mating, thereby increasing the accuracy of the calculation as compared to previous models. For example, when two sires contribute equally to multiply mated broods, only three loci with five equally common alleles are required to provide estimates of f(mm) with high precision. We demonstrate the model with an example addressing the frequency of multiple paternity in small versus large clutches of the endangered Kemp's Ridley sea turtle (Lepidochelys kempi) and show that females that lay large clutches are more likely to have multiply mated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".