Genetic Structure and Diversity among Brook Trout from Isle Royale, Lake Nipigon, and Three Minnesota Tributaries of Lake Superior
Bibliographic record
Abstract
Abstract Brook trout Salvelinus fontinalis from Isle Royale, Michigan, three Minnesota tributaries of Lake Superior, and Lake Nipigon in Ontario were analyzed for genetic variation at 12 microsatellite DNA loci. Analysis of molecular variance, genetic distance measures, and cluster analysis were used to examine the diversity, gene flow, and relatedness among the samples. The diversity estimates for the samples from Isle Royale were similar to those for the samples collected from Minnesota tributaries of Lake Superior, and all estimates were lower than those reported in other studies of brook trout from eastern North America. Genetic differences were detected among the brook trout at Isle Royale, Lake Nipigon, and the Minnesota tributaries of Lake Superior. Further, the population in Tobin Harbor at the eastern end of Isle Royale was distinct from the populations from tributaries at the southwestern end of the island. The Minnesota tributary population formed a group that was genetically distinct from those from Isle Royale and Lake Nipigon. The Isle Royale population should be managed to preserve the genetic and phenotypic variation that distinguishes it from the other brook trout populations analyzed to date.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".