Population Structure of Sockeye Salmon from Russia Determined with Microsatellite DNA Variation
Bibliographic record
Abstract
Abstract The population structure of sockeye salmon Oncorhynchus nerka from Russia was determined from a survey of the variation of 14 microsatellite loci among approximately 3,750 sockeye salmon from 56 populations. The populations from Avacha Bay and the Palana River were quite distinct. Genetic differentiation among the populations surveyed was observed, the mean value of the differentiation index (FST) for all loci being 0.038 (SD = 0.007). Regional structuring of the populations surveyed was observed, multiple populations sampled within a lake being more similar to each other than to populations in other lakes or rivers. River drainage of origin was a significant unit of population structure. Populations spawning in rivers without access to a nursery lake for juvenile rearing displayed greater genetic variation than did populations with access to nursery lakes. Within Kurilskoye Lake, genetic differentation was observed between populations spawning in tributaries to the lake and those populations spawning along beaches within the lake. In the Kamchatka River drainage, genetic differentiation between the Azabachie Lake populations and other populations within the drainage was sufficient to discriminate between natal and nonnatal juveniles rearing in Azabachie Lake.
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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) | 0.000 | 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".