Genetic structure of lake whitefish (Coregonus clupeaformis) in Lake Michigan
Bibliographic record
Abstract
Genetic relationships among lake whitefish ( Coregonus clupeaformis ) spawning aggregates in Lake Michigan were assessed and used to predict a stock or management unit (MU) model for the resource. We hypothesized that distinct spawning aggregates represented potential MUs and that differences at molecular markers underlie population differentiation. Genetic stock identification using 11 microsatellite loci indicated the presence of six genetic MUs. Resolved MUs corresponded to geographically proximate spawning aggregates clustering into genetic groups. Within MUs, analyses suggested that all but one delineated MU was a stable grouping (i.e., no between-population differences), with the exception being the Hog Island – Traverse Bay grouping. Elk Rapids was the most genetically divergent population within Lake Michigan. However, low Fstvalues suggested that moderate to high levels of gene flow occur or have occurred in the past between MUs. Significant tests of isolation by distance and low pairwise Fstvalues potentially led to conflicting results between traditional analyses and a Bayesian approach. This data set could provide baseline data from which a comprehensive mixed-stock analysis could be performed, allowing for more efficient and effective management of this economically and socially important resource.
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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) | 0.001 | 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".