Sound, Sand and Turbulence: Stress in the Boundary Layer Above Evolving Sand Ripples
Bibliographic record
Abstract
This talk will focused on observations of the vertical structure of the flow and Reynolds stress within the turbulent oscillatory boundary layer above evolving sand ripples. The velocity measurements were made with a newly-developed multi-frequency acoustic Doppler profiler, which produces turbulence-resolving velocity profiles through the boundary layer with better than 1 mm range resolution at an ensemble-averaged rate of O(100 Hz). Bed elevation profiles with mm accuracy were obtained with a digital camera and laser light sheet system. Results are presented from laboratory experiments using an oscillatory flow apparatus for both fixed-roughness and mobile beds, and compared to canonical theories for oscillatory boundary layers and semi-empirical relations for the turbulence-related parameters such as the bottom drag coefficient. Questions addressed in the talk will be: (1) how close to the bed can velocities be measured with this system?; and (2), how realistic are the estimates the ensemble-mean structure of the boundary layer, including stress? The talk concluded with a brief discussion of plans for the near future. Presenter Bio Alex Hay’s research interests are in oceanographic processes on the continental shelf from the nearshore to the base of the continental margin, with a particular interest in sediment dynamics. He uses and develops high-frequency acoustic systems for investigating momentum and material fluxes in the bottom boundary layer and for imaging the temporal evolution of the sediment-water interface. After his doctoral studies (of turbidity currents) at the University of British Columbia, he joined the Department of Physics at Memorial University of Newfoundland, and in 1996 took up a chair in ocean acoustic technology in the Department of Oceanography at Dalhousie University. His current research is focused mainly on sediment dynamics in wave-dominated environments.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".