Mussels blow rings: Jet behavior affects local mixing
Bibliographic record
Abstract
Abstract Benthic suspension feeders such as dreissenid mussels (Dreissena polymorpha and D. rostriformis bugensis) are often found in remarkably dense aggregations (i.e. > 105 mussels m− 2), which is surprising, given their high clearance rates and limited mixing within the benthic boundary layer. Results from flow visualization in flow chamber experiments indicate that there is indeed limited mixing around mussel aggregations at low flows and that siphonal jets can increase mixing around and above these aggregations. Using particle image velocimetry (PIV) to further investigate the underlying hydrodynamics of these jets, we characterized differences in velocity and vorticity among four siphonal behaviors (e.g., slow flux, streaming, exhalant jets, and inhalant jets), including both continuous and pulsatile jets, the latter of which generate free vortex rings. Incorporating these hydrodynamic characteristics into a computational fluid dynamic (CFD) model revealed that siphonal jets increased mixing, expressed as vertical diffusivity in the benthic boundary layer. These differences were most pronounced at slow vs. fast cross‐stream velocities, but those differences diminished several body lengths (i.e. 10−1 m to 10−2 m) downstream. The results from PIV measurements and CFD modeling suggest that benthic suspension feeders can influence patterns of local mixing, which would affect mass transport and biogeochemical processes in the near‐bed region. This underscores the need for physical‐biologically linked models to incorporate the behavior of benthic suspension feeding invertebrates.
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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".