Single Nucleotide Polymorphisms Provide Rapid and Accurate Estimates of the Proportions of U.S. and Canadian Chinook Salmon Caught in Yukon River Fisheries
Bibliographic record
Abstract
Abstract As anadromous Chinook salmon Oncorhynchus tshawytscha bound for U.S. and Canadian spawning grounds migrate through the U.S. portion of the Yukon River, they are targeted by several fisheries. To fulfill treaty obligations between the two countries, fishery managers need to know what portion of fish caught in the United States are of Canadian origin. Allozyme markers have been used to assign individuals in mixed fishery samples to U.S. and Canadian portions of the Yukon River; however, these markers are limited by sampling difficulties and by the number of available loci. Microsatellite DNA markers have been considered as an alternative; however, microsatellite data are not readily transportable among laboratories or countries. Here we present the use of single nucleotide polymorphism (SNP) markers that combine the ease of sampling and large potential number of loci of other DNA markers with universally transportable data. Simulations and analyses of known fish suggest that the SNP baseline can be used to assign fish to country of origin with more than 95% accuracy. Assignments based on SNP data are largely concordant with those based on allozyme data. The SNP baseline described here may be used to provide rapid and accurate estimates of the proportions of U.S. and Canadian Chinook salmon caught in Yukon River fisheries.
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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 | 0.001 |
| 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) | 0.001 | 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".