Coherent Doppler profiler measurements of near-bed suspended sediment fluxes for different bed states
Bibliographic record
Abstract
Vertical profiles of vertical turbulence intensity and vertical sediment fluxes were collected by an acoustic coherent Doppler profiler at two locations: Queensland Beach (Nova Scotia), and Duck (North Carolina). Observations of the turbulence intensity over different bed states (irregular ripples, cross ripples, linear-transition ripples, and flat bed) reveal that the near-bed turbulence levels are strongly affected by bed forms. This study examines the mechanisms of distributing suspended sediments and generating near-bed turbulence for the four bed states based on the characteristics of two previously observed mechanisms: diffusion and vortex shedding. Wave-phase averages of turbulence intensity, suspended sediment concentration, and suspended sediment fluxes are compared to vortex shedding and diffusion signatures. Evidence of vortex shedding is found for the low-energy ripples, but no signatures of diffusion are observed. Two diffusion models including a bed stress model, and an eddy diffusion model are found to predict near-bed turbulence levels reasonably well, but predictions are inconsistent with the trend of the data. A vortex-shedding model [J. F. A. Sleath, J. Fluid Mech. 182, 369–409 (1987)] predicts the vertical structure of the turbulence for rippled beds when the ripple wavelength is used as a ripple roughness.
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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 | 0.001 | 0.001 |
| 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".