Wind-generated ambient noise in the deep ocean trenches
Bibliographic record
Abstract
Under the influence of the wind, breaking waves create bubbles, which oscillate as monopoles thus acting as efficient sources of sound. In the deep ocean trenches, bottom reflections are often negligible and the bubble-generated ambient noise is primarily downward traveling. The Deep Sound instrument platform is designed to record the noise on pairs of hydrophones, aligned vertically and horizontally, to depths as great as 11 000 m. Deep Sound consists of a Vitrovex glass sphere, three recovery antennas, a high-performance data acquisition system, inertial navigation, and a CTD plus sound speed sensor, with power provided by lithium-ion batteries. It descends under gravity, and releases a drop weight at depth, at which point it returns to the surface under buoyancy, collecting sound speed and acoustic data on the descent and ascent. Deep Sound has been deployed in the Challenger Deep in the Mariana Trench, the Tonga Trench, the Sirena Deep, and the Philippine Sea. One of the conclusions from the data is that the noise field in the deep trenches conforms to the simple Cron and Sherman theoretical model provided that the local sound speed is used in the computation of the spatial coherence of the noise. [Research supported by ONR.]
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".