Population structure and divergence using microsatellite and gene locus markers in Chinook salmon (<i>Oncorhynchus tshawytscha</i>) populations
Bibliographic record
Abstract
Using different classes of genetic markers can provide insight into the role of selection, as well as a broader context for identifying population differentiation. We used nine microsatellite loci and polymorphisms at eight gene loci (major histocompatibility complex (MHC) classes I and II, growth hormones 1 and 2, transferrin, and immunoglobin heavy-chain) to determine population structure in six coastal populations (Vancouver Island, VI) and five interior populations (Fraser River, FR) of Chinook salmon (Oncorhynchus tshawytscha) in British Columbia, Canada. FSTand [Formula: see text] values for specific VI gene loci were significantly higher than those for the FR and VI microsatellite loci or the FR gene loci. Pairwise microsatellite FSTvalues were correlated with geographic distance across regions, but not using the gene locus marker data. Neighbor-joining cluster analyses showed one VI population as particularly divergent based on the gene locus data, while the VI and FR microsatellite locus and the FR gene locus analyses yielded no anomalous population divergence. The VI MHC class II marker FSTvalues were exceptionally high, indicative of probable directional selection acting on MHC class II. Our results are consistent with local adaptation in Chinook salmon, but the nature of the local adaptation likely differs among regions.
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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 | 0.001 |
| 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".