The Application of Microsatellites for Stock Identification of Yukon River Chinook Salmon
Bibliographic record
Abstract
Abstract In a cooperative project among three agencies, variation at 30 microsatellite loci was surveyed for 19 populations of Chinook salmon Oncorhynchus tshawytscha from the Yukon River. The observed number of alleles per locus ranged from 2 to 63. Loci with a greater number of alleles displayed lower genetic differentiation index (FST) values, but loci with lower FST values also tended to provide more-accurate estimates of stock composition. The observed number of alleles was related to the power of the locus for providing accurate estimates of stock composition of simulated single-population samples. Mean estimated stock compositions for these mixtures ranged from 38.9% for simulations of single loci with fewer than 10 alleles to 85.5% for simulated loci with more than 60 alleles. Reliable population-specific estimation of stock composition was obtained with a minimum of five loci. Comparison of microsatellite stock identification power with an existing nine-locus single-nucleotide polymorphism (SNP) baseline indicated that nine SNPs or a single microsatellite locus can provide accurate and reasonably precise estimates of stock composition to country of origin. In comparisons of population-specific estimation, the nine-SNP baseline was approximately equivalent to a single microsatellite locus with 17–22 alleles. Mixed-stock analysis applying the microsatellite baseline outlined in this study can accurately and precisely allocate Chinook salmon to units useful for management, such as region or major drainage, providing managers with a powerful tool for assessing and regulating fisheries.
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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 | 0.001 |
| 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".