A method to bin alleles of genetic loci that maintains population heterogeneity
Bibliographic record
Abstract
Fishery resources are often studied when individuals from multiple stocks are aggregated. For that reason, mixed-stock analysis (MSA), i.e., estimation of the stock composition of a mixture of individuals, is an important component of many research programs. Although many characteristics can be used in MSA, DNA loci, particularly microsatellites, have become extremely common. Microsatellite loci usually have a greater number of potential expressions, or alleles, than other marker types. A high degree of polymorphism can enhance the power of MSA, but allele proportions are estimated less precisely and rare alleles are absent or observed in very small numbers in typically sized samples. The reduced precision and presence of rare alleles can degrade the performance of some analytic methods. Although the effect can be reduced by binning alleles, which is common, an objective method of doing so has not been available previously. We present a method for binning alleles that reduces the number of rare alleles, largely preserves the genetic relationships observed among stocks, and modestly improves the performance of mixed-stock and individual-assignment analyses. The method is illustrated with data from Yukon River chum salmon (Oncorhynchus keta) and western Alaska Dolly Varden (Salvelinus malma).
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".