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 Dreissena spp. bivalves examined the transport and suspension of pediveliger larvae and larval models in a laboratory flow chamber and larval settlement in Lake Erie. Particle image velocimetry (PIV) measurements in the laboratory, and acoustic Doppler 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".