Improvements to the methods used to measure bubble attenuation using an underwater acoustical resonator
Bibliographic record
Abstract
Active acoustic techniques are commonly used to measure oceanic bubble size distributions, by inverting the bulk acoustical properties of the water (usually the attenuation) to infer the bubble population. Acoustical resonators have previously been used to determine attenuation over a wide range of frequencies (10-200 kHz) in a single measurement, corresponding to the simultaneous measurement of a wide range of bubble sizes (20-300 μm radii). However, there is now also considerable interest in acquiring measurements of bubbles with radii smaller than 16 μm, since these are thought to be important for ocean optics and as tracers for near-surface flow. To extend the bubble population measurement to smaller radii, it is necessary to extend the attenuation measurements to higher frequencies. Although the principles of resonator operation do not change as the frequency increases, the assumptions previously made during the spectral analysis may no longer be valid. In order to improve the methods used to calculate attenuation from acoustical resonator outputs, a more complete analysis of the resonator operation is presented here than has been published previously. This approach allows for robust attenuation measurements over a much wider frequency range and enables accurate measurements from lower-quality spectral peaks.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".