Bibliographic record
Abstract
It is well known that ambient sound is generated by wind through the process of wave breaking and bubble injection. The resulting sound levels are highly correlated with wind speed and, even though the physical process is not fully understood, sound levels can be used to estimate wind speeds with accuracies comparable to other marine wind measurement techniques. It has been noted by several researchers that background sound levels in acoustic Doppler current profiler (ADCP) systems are correlated to wind speeds; however, conventional wisdom would suggest that this signal should be dominated by thermal noise. In this report, background sound levels in 75-, 150-, and 300-kHz ADCP systems have been investigated. Techniques required to convert raw data into absolute sound levels and to adjust these values to estimate representative surface sound levels are presented. Only the background sound levels in the 150-kHz ADCP retain a signal from the surface-generated ambient sound. For these systems, deployment-independent wind speed estimates can be made with an accuracy of 1.5 ± 1.5 m s−1 for wind speeds up to 15 m s−1; accuracies of −0.1 ± 1.4 m s−1 can be achieved when using deployment-specific calibration constants. At higher wind speeds, significant signal attenuation occurs due to the presence of subsurface bubbles; a correction for this attenuation is applied. Wind speeds determined from background sound levels can be combined with the ability of upward-looking ADCPs to extract wind direction to provide wind vector data. There is also a wind speed–dependent signal in the near-surface backscatter levels of the 300-kHz systems. The 300-kHz signal is not associated with background sound levels and is most likely caused by near-surface backscatter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".