The role of turbulence in broadcast spawning and larval settlement in freshwater dreissenid mussels
Bibliographic record
Abstract
The role of turbulence has been shown theoretically to influence external fertilization and larval settlement/transport in benthic invertebrates. This is especially true for turbulence generated by bottom roughness in the near-bed region of lakes. This thesis examined the role of bottom roughness created by the presence of freshwater mussels ('Dreissena polymorpha ' and 'D. bugensis') by addressing three objectives: (1) how fertilization success is influenced by water velocity and near-bed turbulence generated by bottom roughness in a laboratory flow chamber and in the field (Evans Point, Lake Erie); (2) how larval transport and settlement is influenced by this near-bed turbulence in laboratory flow chamber and field (Lake Erie) experiments; and (3) how gamete and larval transport are influenced by the ratio of roughness spacing ([lambda]) to roughness height (' k') roughness height under the mussel patch configuration, which was positively associated with turbulent ejections. Results from the CFD modeling, which incorporated a released scalar as a proxy for gamete and larvae, accurately predicted the flow regimes classified using the ratio of [lambda]/' k' (e.g., < 8 for skimming flow and ~8 for wake interference flow), but this varied with the geometry of the modeled roughness elements. These results indicate that the spatial configuration of bottom roughness, including mussels, determines the flow regime (i.e., skimming vs, wake interference flow), which, in turn affects fertilization success and larval transport/settlement in benthic species.
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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".