Analysis of microsatellite DNA resolves genetic structure and diversity of chinook salmon (<i>Oncorhynchus tshawytscha</i>) in Californias Central Valley
Bibliographic record
Abstract
We use 10 microsatellite DNA markers to assess genetic diversity within and among the four runs (winter, spring, fall, and late fall) of chinook salmon (Oncorhynchus tshawytscha) in California's Central Valley. Forty-one population samples are studied, comprising naturally spawning and hatchery stocks collected from 1991 through 1997. Maximum likelihood methods are used to correct for kinship in juvenile samples and run admixture in adult samples. Through simulation, we determine the relationship between sample size and number of alleles observed at polymorphic microsatellite markers. Most samples have random-mating equilibrium proportions of single and multilocus genotypes. Temporal and spatial genetic heterogeneity is minimal among samples within subpopulations. An FSTof 0.082 among subpopulations, however, indicates substantial divergence among runs. Thus, with the exception of our discovery of two distinct lineages of spring run, genetic structure accords with the diverse chinook life histories seen in the Central Valley and provides a means for discrimination of protected populations.
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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 | 0.000 |
| 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".