Individual-based genotype analysis in studies of parentage and population assignment: how many loci, how many alleles?
Bibliographic record
Abstract
We developed multivatiate analytical models to predict the probability of assigning offspring to parental couples as a function of population size, number of loci, and allelic diversity and the relationships between the probability of allocating individuals to their population of origin as a function of number of loci and allelic diversity. The parentage model predicts that the number of loci and number of alleles contribute interactively to increase assignment success. Given sufficient allelic diversity, a relatively low number of loci is required to achieve high allocation success even for relatively large numbers of possible parents. In contrast, the population model predicts an additive contribution of the number of loci and alleles. There appears to be no significant gain in increasing allelic diversity beyond approximately 6-10 alleles per locus in population assignment studies. Such predictive models should contribute to maximizing the returns of population and parentage assignment studies by increasing our understanding of interactions among the various variables affecting allocation success and by allowing the adjustment a priori of the required level of resolution and, consequently, optimizing the costs-benefits ratio in the use of molecular markers.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".