A New Genotyping-in-Thousands-by-Sequencing Single Nucleotide Polymorphism Panel for Mixed-Stock Analysis of Chum Salmon from Coastal Western Alaska
Bibliographic record
Abstract
Abstract Genetic stock identification is becoming increasingly important in the management of Chum Salmon Oncorhynchus keta from western Alaska due to frequent run failures in recent times. It has been notoriously difficult to distinguish populations of summer-run Chum Salmon among four major regions in coastal western Alaska: Norton Sound, lower Yukon River, Kuskokwim River, and Nushagak River. Here we developed and evaluated a panel of single nucleotide polymorphism (SNP) markers designed to coamplify using the genotyping-in-thousands by sequencing (GT-seq) method, which would greatly enhance the efficiency of genotyping samples from baseline populations and from mixed-stock fisheries or incidental catches. We selected 479 SNPs in 355 amplicons from ~30,000 candidate SNPs for the GT-seq panel. Evaluations using single-stock and realistic fishery mixture simulations indicated that the panel was able to satisfactorily distinguish Norton Sound from the other regions to the south but was unable to distinguish among lower Yukon, Kuskokwim, and Nushagak rivers with accuracy >90%. The low degree of population structure among Chum Salmon in this region, described in previous studies and confirmed with tens of thousands of SNPs here, means that genetic stock identification will be inadequate to guide management decisions at the spatial scale desired by stakeholders and fishery managers in the region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".