Investigating high frequency ADCP capabilities in measuring turbulence in tidal channels
Bibliographic record
Abstract
Tidal channels are highly dynamic regions of the coastal ocean that exhibit strong turbulent behaviour. High resolution measurements of vertical turbulence and mean horizontal current speeds in these regions are required by the in-stream tidal turbine industry for site characterisation. Such data can be used by industry to improve estimates of tidal dissipation rates, energy generation potential and predictions of stress on critical hardware components. The in-situ oceanographic instruments that are used to make measurements of turbulence, such as shear-probes or Doppler velocimeters can be difficult to position in the presence of large horizontal current speeds (up to 3 m/s) that are characteristic of these active tidal channels. Sea floor mounted acoustic Doppler Current Profilers (ADCP) can remotely collect turbulence data for extended periods of time and provide an alternative to in-situ instruments. However, the accuracy and limitations of these ADCP turbulence measurements in the presence of high current speeds needs to be quantified. These limitations are explored using a model of acoustic backscatter integrated with output from a Large Eddy Simulation (LES) of an idealised tidal channel measuring 1 km × 200 m × 30 m. We simulate the direct acoustic measurement of the turbulent vertical velocities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".