Dense SNP panels resolve closely related Chinook salmon populations
Bibliographic record
Abstract
Chinook salmon (Oncorhynchus tshawytscha) are migratory fish that are highly valued for subsistence, sport, and commercial fisheries throughout their native range. Populations of Chinook salmon in western Alaska have exhibited long-term declines, leading to restrictions on harvests. Management priorities require greater resolution for genetic stock identification (GSI) than is available with current methods. We leveraged RADseq, TaqMan, and GT-seq data originating from multiple sources, collected through time, to develop a set of GT-seq panels containing 1092 single nucleotide polymorphisms (SNPs) that improved GSI resolution in western Alaska for at-sea and in-river sampling. We generated a dense linkage map to ensure that markers selected for panels spanned the entire genome. In addition, we identified multiple RADseq markers that were associated with sex; these aligned to a 5-centimorgan (cM) region on the sex chromosome. Finally, we developed a bioinformatic pipeline to streamline analysis of GT-seq data that is capable of genotyping microhaplotypes and paralogs, both of which can improve GSI resolution over traditional single-SNP data. Our panels and pipeline provide tools for management agencies to rapidly and easily analyze large-scale genotyping projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".