Allele frequency stability in large, wild exploited populations over multiple generations: insights from Alaska sockeye salmon (<i>Oncorhynchus nerka</i>)
Bibliographic record
Abstract
We genotyped nuclear and mitochondrial single nucleotide polymorphisms (SNPs) in six paired archived and contemporary collections of Alaskan sockeye salmon ( Oncorhynchus nerka ) to evaluate the stability of allele frequencies over 25–42 years (4.9–8.4 generations). First, our results show that temporal changes were dramatically (between 40- and 250-fold) smaller than spatial differences in allele frequencies when based on nuclear SNPs. Second, the magnitude of temporal change was consistent with a model of genetic drift: (i) SNPs with high levels of differentiation (large θ) and candidates for diversifying selection were not more likely to show significant temporal changes than small-θ SNPs; and (ii) the fraction of single-locus significant tests was consistent with theoretical predictions relating sample size and the annual number of breeders (Nb). Third, estimates of Nbwere bound by infinitely large upper 95% confidence intervals, except for one paired collection with unique life-history attributes of both a smoltification phase and generation time shorter than the other paired collections. Use of multigenerational SNP data sets seems a safe practice in management of Alaska sockeye salmon that could be extended to other large, wild aquatic 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 | 0.001 | 0.002 |
| 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".