Connectivity among populations of pygmy whitefish (<i>Prosopium coulterii</i>) in northwestern North America inferred from microsatellite DNA analyses
Bibliographic record
Abstract
We studied microsatellite DNA variation in 15 populations of northwestern North American pygmy whitefish ( Prosopium coulterii (Eigenmann and Eigenmann, 1892)), an enigmatic freshwater fish thought to be highly fragmented by residency in deep, cold postglacial lakes. Population subdivision (θ) across 10 loci was 0.12 (P < 0.001) across samples, but one western Alaskan population was more divergent than all others (θ = 0.31–0.41, P < 0.001). Within the Williston Reservoir watershed (WRW), θ averaged 0.08 (P < 0.001) and was positively associated with both the geographic distance between localities (r2= 0.36, P < 0.001) and the number of branch points interconnecting them (r2= 0.33, P < 0.001). Differentiation among populations was modeled as the sum of the genetic distances for the stream sections interconnecting them (r2= 0.74). Differences among subwatersheds with the WRW accounted for 5.1% of the total variation in allele frequencies (P < 0.001). Assignment tests suggested limited movement among lakes, with most inferred dispersal between adjacent watersheds. Coalescent analysis strongly supported a gene flow–drift equilibrium model of population structure over a drift-only model. Effective management of diversity in pygmy whitefish requires the maintenance of stream networks that interconnect lakes within a watershed.
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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 | 0.000 | 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".