Nested analysis of genetic diversity in northwestern North American char, Dolly Varden (<i>Salvelinus malma</i>) and bull trout (<i>Salvelinus confluentus</i>)
Bibliographic record
Abstract
Partitioning within-species genetic diversity is fundamental to conservation of the bioheritage, current viability, and evolutionary potential of individual taxa. We conducted a hierarchical analysis of genetic diversity in Dolly Varden (Salvelinus malma) and bull trout (Salvelinus confluentus) involving analysis of hybrid zones between Dolly Varden and bull trout, analysis of phylogenetic structure within species across their native ranges using mitochondrial DNA, and a microsatellite DNA survey of population subdivision of bull trout within single watersheds. Our analyses documented hybridization and some introgression between Dolly Varden and bull trout across a geographically widespread zone of secondary contact between the two species. Both species were subdivided into two major mtDNA lineages, and one lineage in Dolly Varden may have arisen through introgression with bull trout. Bull trout have low levels of microsatellite diversity within populations, but there was substantial interpopulation variation in allele frequencies. Allele frequency distributions suggested that recent, severe bottlenecks occur frequently in bull trout populations. Our results illustrate partitioning of genetic variation at distinct levels of biological organization (species, phylogeographic lineages, local populations), and we address how such nested variation is fundamental to conservation of biodiversity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".