Fine-scale population genetic structure of the yellow perch<i>Perca flavescens</i>in Lake Erie
Bibliographic record
Abstract
Discerning the genetic basis underlying fine-scale population structure of exploited native species and its relationship to management units is a critical goal for effective conservation. This study provides the first high-resolution genetic test of fine-scale relationships among spawning groups of the yellow perch Perca flavescens . Lake Erie yellow perch stocks comprise valuable sport and commercial fisheries and have fluctuated extensively owing to highly variable annual recruitment patterns. Fifteen nuclear DNA microsatellite loci are analyzed for 569 individuals from 13 primary Lake Erie spawning sites and compared with those spawning in Lakes St. Clair and Ontario. Additional comparisons test for possible genetic differences between sexes and among size–age cohorts. Results demonstrate that yellow perch spawning groups in Lake Erie are genetically distinguishable and do not differ between sexes and among age cohorts. Population genetic structure does not follow a genetic isolation with geographic distance pattern, and some spawning groups contribute more to overall lake-wide genetic diversity than do others. Partitioning of the yellow perch’s genetic structure shows little congruence to lake basins or to current management units. Our findings underlie the importance of understanding spawning habitat and behavior to conserve the genetic stock structure of a key fishery.
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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 | 0.000 |
| 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".