Navigating turbulence: the effects of eddy size on the swimming performance of walleye ( <i>Sander vitreus</i> ) larvae
Bibliographic record
Abstract
Walleye (Sander vitreus) populations experience substantial interannual fluctuations driven largely by high rates of larval mortality. To investigate the potential mechanisms underlying recruitment in walleye larvae, we assessed the effect of turbulence on larval swimming performance in a recirculating flow chamber. We measured the critical swimming speeds (Ucrit) of larvae throughout their first 5 weeks of development in response to increasing levels of turbulence and varying eddy sizes, generated through controlled water flow and the use of grid turbulence. As early as the first week post hatch, larvae exhibited a rheotactic response, demonstrating the ability to resist and swim against turbulent flows to some extent. Measured Ucrit increased with larval total length (LT; or age), and was lower in the grid-turbulence treatment, in which both the turbulence and the size of eddies were constrained by the grid spacing. Conversely, the relative critical swimming speeds based on body length (Ucrit,rel) declined with LT; swimming performance declined significantly when the eddy diameter approached approximately two-thirds of the larvae's total length. This ratio declined with age in the no-grid treatment, but was relatively constant in the grid treatment. Our results suggest that the scale of turbulence, rather than the magnitude of turbulent energy, has a greater influence on swimming performance. These findings highlight the importance of considering eddy length scale when assessing the swimming performance of fish larvae. Additionally, the swimming parameters established in this study can inform more realistic larval dispersal models for walleye as well as fisheries management decisions.
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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 | 0.000 |
| 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 | 0.000 |
| 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".