Genetic analysis of fish genomes and populations: allozyme variation within and among Atlantic salmon from Downeast rivers of Maine
Bibliographic record
Abstract
Analysis of genetic variation within and among samples of naturally produced Atlantic salmon (n = 372) from 7 Maine (U.S.A.) and one Canadian river were conducted based on 54 allozyme loci. Eight of the 54 loci proved polymorphic, and estimated heterozygosities (HS) based on all loci ranged from 0·012 to 0·026 (mean = 0·021, s.e. = 0·002). Only one of 56 tests revealed genotypic proportions that deviated significantly from Hardy–Weinberg expectations. Genetic distances (D) between samples ranged from 0·002 to 0·022. No obvious association existed between genetic and geographic distances. Cluster analysis of genetic distances revealed the Dennys River sample as the most differentiated when all samples were included in the analysis, though bootstrap support of the cluster analysis was generally weak. G‐tests revealed significant differences in allele frequencies among samples at five of the polymorphic loci, and the G‐value summed over all loci also indicated significant differences among samples. FST values indicated that 3·4% of the total genetic diversity was due to variability among samples, while 96·6% was due to variability within samples. These results indicate that the Atlantic salmon analyzed in this study had levels of genetic variability and differentiation among samples comparable to native populations from other areas collected across a similar geographic range.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".