Microsatellite Stock Identification of Chum Salmon on a Pacific Rim Basis
Bibliographic record
Abstract
Abstract The variation at 14 microsatellite loci was analyzed for more than 53,000 chum salmon Oncorhynchus keta from 381 locations ranging from Korea to Washington State and used to estimate the stock composition of mixed-stock fishery samples. High resolution of the samples was possible, the number of reporting groups being distributed as follows: Korea = 1, Japan = 7, Russia = 8, Alaska = 15, Canadian Yukon River = 5, British Columbia = 16, and Washington State = 5. 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. Approximately 800 alleles were observed across the 14 microsatellites, providing the basis for high-resolution stock identification. Analysis of known-origin samples indicated that accurate regional estimates of stock composition were obtained. The estimated stock compositions of mixed-fishery samples from coastal Japan, the Sea of Okhotsk, the western Pacific Ocean, the Gulf of Alaska, and coastal British Columbia were quite different among samples and clearly reflected the presence of local populations. Microsatellites have provided the ability to obtain accurate estimates of the stock composition of chum salmon from many locations in the Pacific Rim.
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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".