Genetic divergence among broodstocks of Arctic charr Salvelinus alpinus in eastern Canada derived from the same founding populations
Bibliographic record
Abstract
We examined the amount and distribution of molecular variation at microsatellite loci in 21 broodstocks of Arctic charr Salvelinus alpinus derived from the Fraser River, Labrador and the Nauyuk Lake, Nunavut, Canada. Our goal was to assess the amount of genetic diversity and differentiation as broodstocks are subdivided and propagated in different hatcheries and grow-out facilities. We observed significant heterogeneity across pairs of ancestral and descendant broodstocks in the mean numbers of alleles at microsatellite loci. We detected a significant decrease in the observed heterozygosity between ancestors and descendants but the amount of decrease did not depend on either the degree of removal from the wild (number of sequential transfers) or the strain (Fraser vs. Nauyuk). Based on allele frequency distributions, there was little genetic evidence of bottlenecks during the creation of subsequent broodstock populations after the initial founding events. All broodstock samples were significantly differentiated from each other but those within the same strain were more similar to each other than to broodstocks from different strains. Broodstocks from the Nauyuk Lake broodstocks showed greater differentiation from each other than did Fraser River broodstocks, which could be attributed to differences in the number of founders.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".