Evidence across multiple scales for offshore transport of yellow perch (<i>Perca flavescens</i>) larvae in Lake Michigan
Bibliographic record
Abstract
Most freshwater fishes have short pelagic early life stages. Lake Michigan presents an interesting scenario for yellow perch (Perca flavescens), a species with a long pelagic larval stage that may not be well adapted to lakes with an expansive pelagic environment and extensive offshore transport. We investigated the possibility that early life stages of yellow perch were transported well offshore from their nearshore spawning grounds and explored whether food resources were more or less favorable offshore. To determine the extent to which pelagic age-0 yellow perch moved offshore, we sampled at multiple scales ranging from local (<2 km) to across the lake (>120 km). Evidence of offshore movement by fish larvae occurred at each scale. Yellow perch larvae were quickly transported offshore from nearshore spawning sites and remained in the offshore pelagia to sizes of at least 30 mm. Zooplankton density was greater offshore than nearshore, suggesting that pelagic age-0 yellow perch find improved food resources offshore compared with their nearshore spawning sites. Currents operating at oceanographic scales likely influenced the offshore movement of pelagic age-0 yellow perch in Lake Michigan. These currents, coupled with prey availability for pelagic age-0 individuals, may influence the recruitment success of this species.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".