Yellow perch (Perca flavescens) stock structure in Lake Michigan: an analysis using mark–recapture data
Bibliographic record
Abstract
To evaluate the stock structure of yellow perch ( Perca flavescens ) in the southern basin of Lake Michigan and in Green Bay, we analyzed recaptures from a lake-wide mark–recapture study implemented from 1996 to 2001 to infer the range and pattern of movement and spawning-site fidelity. Yellow perch generally moved south along the western shoreline, west along the southern shoreline, and north along the eastern shoreline during summer and non-summer months; the magnitude of movement was greater after spawning. Spawning yellow perch frequently returned to the same site, with 35%–80% of recaptured individuals returning to their marking site. Results from multiple tagging sites within Illinois indicated that spawners may return to larger areas rather than to specific sites, suggesting that large spawning complexes exist. Despite strong fidelity in some areas, straying was evident from all sites during spawning, resulting in mixing throughout the southern basin. Such mixing could promote gene flow and diminish stock differentiation. Dispersal of yellow perch within the southern basin of Lake Michigan occurred regularly across adjacent management boundaries. Therefore, adjacent jurisdictions may wish to consider re-examining their regulations based on this information to ensure consistent, complementary regulations that incorporate the movement patterns of yellow perch.
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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 | 0.001 | 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".