Mussel larval responses to turbulence are unaltered by larvalage or light condition
Bibliographic record
Abstract
Larval responses to hydromechanical cues potentially have important effects on larval dispersal and settlement. This study examined the behavior of mussel larvae (Mytilus edulis) in laboratory-generated turbulence representative of nearshore currents. We video recorded the behavior of early- and late-stage veligers in a grid-stirred tank at five turbulence levels under light and dark conditions. Water velocities and kinetic energy dissipation rates were measured using particle image velocimetry and acoustic Doppler velocimetry. We characterized the vertical velocity distributions for sinking, hovering, and swimming modes in still water and calculated the average larval behavioral velocity in turbulence. In still water, young larvae had more positive (upward) velocities than old larvae, and both stages had more positive velocities in light than in dark. In turbulence, the mean larval vertical velocity varied from positive at low dissipation rates to negative at dissipation rates above a threshold of 8.3 £ 1022 cm2 s23. At this threshold, the Kolmogorov length scale (h ¼ 590mm) was two to three times the mean larval shell lengths (171–256mm), implying that turbulence is detectable even by larvae that are smaller than the smallest eddies. Responses to turbulence were unaffected by larval age or light conditions and contributed substantial behavioral variation. By sinking in strong turbulence, mussel larvae could increase their flux to the bed in energetic coastal flows, particularly over rough substrates like mussel beds. The response to turbulence by early-stage larvae will also affect their dispersal and may help larvae remain near coastal populations.
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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) | 0.001 | 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".