Evolution of introduced Chinook salmon (<i>Oncorhynchus tshawytscha</i>) in Lake Huron: emergence of population genetic structure in less than 10 generations
Bibliographic record
Abstract
Abstract – Population genetic structure was detected in Chinook salmonOncorhynchus tshawytschain their non‐native range of Lake Huron using microsatellite DNA. All Chinook salmon in this system descend from Green River, Washington cohorts, originally transplanted to Michigan hatcheries in the late 1960s. We tested for population genetic differentiation of age 0 fish collected from 13 rivers and two hatcheries in 2007. The amount of genetic differentiation among collection sites was low but statistically significant, withFSTvalues ranging from 0.036 to 0.133 andRSTvalues ranging from 0.008 to 0.157 for specific loci. Based on pairwiseFSTandRSTvalues and Bayesian cluster analysis, the Maitland River population in the Main Basin of Lake Huron was genetically distinct from the remaining collection sites. Based on analysis of bycatch data from commercial gill net fisheries, Chinook salmon likely colonised the Main Basin by 1975 (10 generations ago) and the North Channel and southern Georgian Bay regions by 1980 (eight generations ago). Thus, population genetic structure has emerged in Lake Huron Chinook salmon in <10 generations.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".