Resolving size distributions of bubbles with radii less than 30 <i>μ</i>m with optical and acoustical methods
Bibliographic record
Abstract
Many studies have investigated bubble size distributions in the ocean, but the measured size range does not normally extend to bubbles with a radius below 20 μm. Bubbles smaller than this are thought to have a significant effect on the optical properties of the ocean, potentially affecting remotely sensed measurements of ocean color and the optical detection of particulates and dissolved matter. Such optical data are becoming the major source of oceanic information about algal blooms, primary productivity, sediment loading and the spread of pollutants. The challenges associated with measuring these bubbles are difficulty of calibrating sensors with independent bubble size measurements and lack of knowledge about the organic coating on the bubbles. This paper describes simultaneous oceanic measurements of these small bubbles using independent optical and acoustical techniques. These measurements agree well, and an investigation of the bubble coating parameters was made. Both the optical and acoustical properties of bubbles are affected by this organic coating, and a comparison of these measurements narrows down the choice of possible coating parameters. Our results suggest that the bubbles measured in this study were likely to have a coating with a thickness of 10 nm and a refractive index of 1.18, and that the coating thickness is the more important parameter for optical inversions. The research described here is the first attempt to constrain these parameters in the ocean using two independent techniques and suggests that further studies of this type could result in significant insight into oceanic bubble coatings.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".