Population structure of chum salmon and selection on the markers collected for stock identification
Bibliographic record
Abstract
Abstract Genetic stock identification (GSI) is a major management tool of Pacific salmon ( Oncorhynchus Spp.) that has provided rich genetic baseline data of allozymes, microsatellites, and single nucleotide polymorphisms (SNPs) across the Pacific Rim. Here, we analyzed published data sets for adult chum salmon ( Oncorhynchus keta ), namely 10 microsatellites, 53 SNPs, and a mitochondrial DNA locus (mtDNA3, control region and NADH-3 combined) from 495 locations in the same distribution range ( n = 61,813). TreeMix analysis of the microsatellite loci identified the highest level of genetic drift towards Japanese/Korean populations and suggested two admixture events from Japan/Korea to Russia and the Alaskan Peninsula. The SNPs had been purposively collected from rapidly evolving genes to increase the power of GSI. The highest expected heterozygosity was observed in Japanese/Korean populations for microsatellites, whereas it was highest in Western Alaskan populations for SNPs, reflecting the SNP discovery process. By regressing the SNP population structures on those of the microsatellites, we estimated the selection on the SNP loci according to deviations from the predicted structures. Specifically, we matched the sampling locations of the SNPs with those of the microsatellites according to geographical information and performed regression analyses of SNP allele frequencies on the two coordinates of multi-dimensional scaling (MDS) of matched locations obtained from microsatellite pairwise F ST values. The MDS first axis indicated a latitudinal cline in American and Russian populations, whereas the second axis found a differentiation of Japanese/Korean populations. The top five outlier SNPs were mtDNA3 (combined locus of the control region and NADH-3), U502241 (unknown), GnRH373, ras1362, and TCP178, which were consistently identified by principal component analysis. We summarized the functions of the 53 nuclear SNPs and mtDNA3 locus by referring to a gene database system and discussed the functions of the outlier SNPs and fitness of chum salmon.
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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 | 0.000 |
| 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".