Managing mixed-stock fisheries: genotyping multi-SNP haplotypes increases power for genetic stock identification
Bibliographic record
Abstract
A common challenge for fisheries management is resolving the relative contribution of closely related populations where accuracy of genetic assignment may be limited. An overlooked method for increasing assignment accuracy is the use of multi-SNP (single nucleotide polymorphism) haplotypes rather than single-SNP genotypes. Haplotypes increase power for detecting population structure, and loci derived from next-generation sequencing methods often contain multiple SNPs. We evaluated the utility of multi-SNP haplotyping for mixture analysis in western Alaska Chinook salmon (Oncorhynchus tshawytscha). Multi-SNP haplotype data increased the accuracy of mixture analysis for closely related populations by up to seven percentage points relative to single-SNP genotype data for a set of 500 loci; 90% accuracy was achievable with as few as 150 loci with multi-SNP haplotypes but required at least 300 loci with single-SNP genotypes. Individual assignment to reporting groups showed an even greater increase in accuracy of up to 17 percentage points when multi-SNP haplotypes were used. Haplotyping multiple SNPs shows promise to improve the accuracy of assigning unknown fish to population of origin whenever haplotype data are available.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".