Pacific Salmon: Ecology and Management of Western Alaska’s Populations
Bibliographic record
Abstract
Abstract.—Microsatellite and major histocompatibility complex (MHC) variation was surveyed to evaluate population structure and the potential for genetic stock identification in chum salmon Oncorhynchus keta populations largely bordering the eastern Bering Sea and northwestern Gulf of Alaska. Variation at 14 microsatellite loci and one MHC locus was surveyed for 59 populations in the study. The genetic differentiation index (Fst) over all populations and loci was 0.023, with individual locus values ranging from 0.007 to 0.058. At least 10 regional stocks were observed in the survey area, with populations from Kotzebue Sound (mean Fst value usually >0.02 in regional comparisons) and the Alaska Peninsula (Fst >0.03) the most distinct of Alaskan populations surveyed. For stock identification applications incorporating DNA variation, chum salmon sampled in rivers’ tributaries to the eastern Bering Sea were classified into the following regions: Kotzebue Sound, Norton Sound, lower/middle Yukon River, Tanana River, Kuskokwim River, Nushagak River, north/central Bristol Bay, southwest Bristol Bay, northern Alaska Peninsula/Aleutian Islands, southwest Alaska Peninsula, and southeast Alaska Peninsula. For simulated single-region mixtures, estimated regional stock compositions were generally above 90% for the previously-listed regions except for the Kuskokwim River, Nushagak and north/central Bristol Bay regions. Incomplete characterization of DNA variation for populations in those regions most likely contributed to the lower accuracies of estimated stock compositions in the simulated mixtures. Estimated regional stock compositions of simulated samples comprising fish from several regions were within 1–3% of actual values provided that no contributions from the three under-represented regions were included in the simulated samples. Microsatellite and MHC variation has the potential to provide accurate estimates of regional stock composition for chum salmon fisheries in the Bering Sea and northern Gulf of Alaska.
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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 | 0.000 |
| 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 | 0.000 |
| 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".