Effects of near‐bed turbulence on the suspension and settlement of freshwater dreissenid mussel larvae
Bibliographic record
Abstract
Summary Larval transport and settlement in benthic invertebrates is theorised to be influenced by bottom roughness and the hydrodynamic forces that roughness generates near the bed. This study of freshwater D reissena spp. bivalves examined the transport and suspension of pediveliger larvae and larval models in a laboratory flow chamber and larval settlement in L ake E rie. Particle image velocimetry ( PIV ) measurements in the laboratory, and acoustic D oppler velocimetry ( ADV ) measurements in the field, were used to estimate small‐scale turbulence in the near‐bed environment over differing roughness. Quadrant analysis was used to determine the frequency of turbulent sweeps and ejections, and the extent of roughness flow regime (skimming versus wake interference flow) was noted to understand the determinants of larval transport and settlement. Skimming flow generated above bottoms with high mussel densities had significantly lower suspended transport (i.e. suspension off the bottom in the flow chamber; 3.31 ± 1.14%) and lower larval settlement in the field (1526 ± 80 larvae m −2 per day) compared to low mussel densities (6.63 ± 1.54% and 1853 ± 47 larvae m −2 per day). Conversely, wake interference flow indicated by high frequencies of turbulent sweeps and ejections generated by the roughness due to mussel patches resulted in high suspended transport in the flow chamber (i.e. via ejections) and the highest larval settlement in the field (i.e. via sweeps; 1943 ± 59 larvae m −2 per day). The spatial configuration of mussel roughness influenced the creation and magnitude of skimming versus wake interference flow, which can inhibit or enhance larval settlement, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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; both teacher heads agree on what is shown here.
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".