Acoustic backscatter from bubbles as a (quasi)passive tracer of turbulent mixing in high-flow tidal channels
Bibliographic record
Abstract
In high-flow tidal channels, the water column tends to be well-mixed vertically due to the high levels of turbulence. Under favorable circumstances, such as those in which wind waves incident at the channel entrance from the adjoining open ocean or large embayment are opposed by the tidal flow, the waves steepen and break as they propagate upstream, resulting in bubble injection at the sea surface. These bubbles are then mixed downward by the tidally generated turbulence, resulting in pronounced, surface-connected, downward-propagating plumes of high backscatter in records from bottom-mounted upward-looking acoustic Doppler current profilers, typically operating at 100s of kHz. Letting the backscatter amplitude represent a pseudo-concentration, C, we demonstrate that the vertical turbulent diffusivity, K, can be estimated from the vertical turbulent flux i < c′w′> along the axis of the vertical beam, and the mean vertical gradient, dC/dz. Intriguingly, and despite the fact that bubbles are positively buoyant, the resulting estimates of K are very close to the values of momentum diffusivity required to obtain good agreement between the observed tidal velocities and a data-validated numerical tidal circulation model for the particular channel in question
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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".