The influence of bubble clouds on acoustic propagation in the surf zone
Bibliographic record
Abstract
High-frequency propagation close to an active surf line is explored with 12and 100-kHz propagation paths together with measurements of bubble clouds, bubble size distributions, and waves. Breaking waves inject massive bubble plumes that are mixed downwards from the roller region by intense turbulence. If these injections follow one another at intervals less than the time taken for the bubbles to rise to the surface, acoustic signals will be continuously blocked, forming an acoustical barrier that effectively inhibits any propagation. Occasionally, waves break seaward of this barrier. In this case, dense bubble clouds are mixed down beneath the air entrainment zone, but there is sufficient time for them to disappear before succeeding breakers, allowing intermittent high-frequency propagation recharge the bubble field. The duration and shape of signal dropouts are then determined by the selective removal of bubbles by buoyancy and dissolution. In addition to turbulence created by the air entrainment process, a lower level of continuous background turbulence may be generated by interaction of residual currents with the wave boundary layer. Our observations illustrate the variable character of acoustic blocking by bubble clouds and serve as a basis for quantitative analysis of these effects with a 2D propagation model coupled to 2D models of bubble cloud evolution and background turbulence.
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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 | 0.001 |
| 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) | 0.001 | 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".