Estimation of Stock Composition and Individual Identification of Chinook Salmon across the Pacific Rim by Use of Microsatellite Variation
Bibliographic record
Abstract
Abstract Variation at 13 microsatellite loci was surveyed for over 52,000 Chinook salmon Oncorhynchus tshawytscha sampled from 325 localities ranging from Russia to California; the variation was applied to estimate stock composition in mixed‐stock fishery samples. A rapid increase in the accuracy of estimated stock composition in simulated mixtures with respect to population sample size was observed for sample sizes of up to about 75 individuals, at which point a 90% accuracy of assignment to population was achieved. The number of alleles observed at a locus was related to the power of the locus in providing accurate estimates of the stock composition of single‐population mixtures. In analysis of single‐population mixtures where the Pacific Rim baseline was used for estimation of stock identification, 75% accuracy for the average population was achieved by employing approximately 55 alleles in the analysis. Increasing the accuracy of the estimated stock composition to 90% for the average population required approximately 350 microsatellite alleles. The precision of estimated stock composition increased rapidly for approximately the first 100 alleles used; standard deviations declined from 20.0% to 8.0%. Analysis of known‐origin samples indicated that accurate regional estimates of stock composition were obtained. The accuracy of assigning individuals to a specific region or river drainage averaged 84% for 54 populations in multipopulation samples. The estimated stock compositions of mixed‐fishery samples from northern and southern locations in British Columbia were quite different among samples and reflected whether samples were derived from migrating or resident Chinook salmon. Microsatellites have the ability to provide accurate estimates of stock composition from many fisheries in the Pacific Rim distribution of Chinook salmon.
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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 | 0.002 |
| 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 | 0.000 | 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 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".