Parentage analysis with incomplete sampling of candidate parents and offspring
Bibliographic record
Abstract
Many breeding systems include 'multiple mating' in which males or females mate with multiple partners. We identify two forms of multiple mating: 'single-sex', where the next-generation individuals (NGIs) are the product of multiple mating by one sex; and 'two-sex', where the NGIs are the product of multiple mating by both sexes. For both mating systems we develop models that estimate the proportion of NGIs that is fathered (paternity) or mothered (maternity) by the putative parents. The models only require genetic data from the parent or parents in question and the sample of NGIs, as well as an estimate of population allele frequencies. The models provide unbiased estimates, can accommodate loci with many alleles and are robust to violations of their assumptions. They allow researchers to address intractable problems such as the parentage of seeds found on the ground, juvenile fish in a stream, and nestlings in a communal breeding bird. We demonstrate the models using genetic data from a nest of the bluegill sunfish Lepomis macrochirus, where the NGIs may be from multiple females that have spawned with multiple males from different life histories (cuckolder and parental).
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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".