A new, standardized international Pacific Rim baseline for genetic stock identification (GSI) of Chinook Salmon
Bibliographic record
Abstract
Abstract Objective Genetic stock identification (GSI) can be an effective tool for fisheries management, but development of reference baselines for species with broad geographic distributions can be challenging. Mixed-stock fisheries for Chinook Salmon Oncorhynchus tshawytscha have utilized GSI analyses for decades with various genetic baselines, but these have largely become outdated with advances in technology that enable more efficient genotyping. Thus, our goals were to (1) create nested baselines of genotypic data for Chinook Salmon throughout their entire natural range using existing data from multiple sources and (2) evaluate the utility of those nested baselines to conduct accurate hierarchical GSI of mixture proportions or the stock identification of individual fish. Methods In this study, we compiled a large genetic baseline of single-nucleotide polymorphism (SNP) markers for 389 populations that encompass the entire geographic range of Chinook Salmon. We used cross validation and realistic mixture simulations to test the accuracy of the baseline in generating GSI estimates. Result We demonstrated that a multi-tiered assignment approach can provide high accuracy at both tier 1 (broadscale, with three coastwide reporting groups; 97.8% mean accuracy) and tier 2 (fine-scale regional reporting groups; up to 97.7% mean accuracy) levels. Realistic mixture simulations showed that this multi-tiered approach can provide highly effective GSI results for several common mixed-stock fisheries applications in the Pacific Ocean. Conclusion This new SNP baseline and the multi-tiered assignment approach provide the most comprehensive rangewide GSI baseline for Chinook Salmon over any previous application and enable highly accurate estimates for GSI purposes.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".