River-plume use during the pelagic larval stage benefits recruitment of a lentic fish
Bibliographic record
Abstract
Similar to coastal marine systems, Lake Erie exhibits open-water river plumes that differ physicochemically and biologically from surrounding waters. To explore their importance to yellow perch ( Perca flavescens ) recruitment in western Lake Erie, we tested two related hypotheses: (i) contributions of larvae to the juvenile stage (when recruitment is set) would be greater from nutrient-rich Maumee River plume (MRP) waters than from less-productive non-MRP waters; and (ii) warmer temperatures and higher zooplankton (prey) production in the MRP (versus non-MRP waters) would underlie this expected recruitment difference through “bottom-up” effects on larval growth. Peak larval yellow perch density was 10-fold and 5-fold less in the MRP than in non-MRP waters during 2006 and 2007, respectively. However, otolith microchemical analyses demonstrated that disproportionately more juvenile recruits emanated from the MRP than from non-MRP waters during both years. Although temperature and zooplankton production were higher in the MRP than in non-MRP waters during both years, observed recruitment differences were not definitively linked to bottom-up effects. Top-down effects also appeared important, as high turbidity in the MRP may offer a survival advantage by reducing predation mortality on larvae. Our research highlights the need to better understand biophysical coupling in freshwater systems and demonstrates how stochastic tributary inputs can influence fish recruitment.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".