DNA-Level Variation of Sockeye Salmon in Southeast Alaska and the Nass and Skeena Rivers, British Columbia, with Applications to Stock Identification
Bibliographic record
Abstract
Abstract The variation at 14 microsatellite loci and 1 major histocompatibility complex locus was surveyed in 12,000 sockeye salmon Oncorhynchus nerka from 35 populations in the Skeena and Nass River drainages in British Columbia and 20 populations from Southeast Alaska. Genetic differentiation among populations was observed, with an overall FST of the microsatellite loci of 0.104. Evaluation of the utility of the microsatellite loci for stock identification applications indicated that the accuracy and precision of the estimated stock compositions generally increased as the number of observed alleles at the loci increased. Analysis of simulated mixtures of sockeye salmon from the Nass River, Skeena River, and Southeast Alaska indicated that DNA variation provides a practical way to estimate stock composition, yielding population and regional estimates within 1–3% of the actual values. The validity of the results from the simulated mixtures was evaluated with an analysis of a sample of known origin. Estimated stock compositions on a regional basis (derived from application of a 203-population baseline) were within 1% of the actual contributions, and for the Southeast Alaska component the individual population estimates were generally within 0.5% of the actual contributions. Analysis of fishery samples from a fishery near Tree Point in Southeast Alaska indicated that the major contributors to the fishery in 2001 were sockeye salmon of Nass River, Skeena River, and Southeast Alaska origin, with some components from the Stikine River. Analysis of fishery samples from a fishery near the entrance to Portland Inlet in northern British Columbia indicated that sockeye salmon of Nass River origin dominated the fishery in 2002. Thus, DNA variation provides an effective method for sockeye salmon stock identification.
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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 | 0.000 | 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".