The depth dependence of ambient noise in deep ocean trenches
Bibliographic record
Abstract
A series of experiments aimed at measuring the power spectrum and vertical and horizontal noise coherence (directionality) in the deep ocean were carried out between 2009and 2015 using a family of autonomous instrument platforms named “Deep Sound.” Deep Sound is a free-falling acoustic recorder designed to descend from the ocean’s surface to a pre-assigned depth where it drops an iron weight and returns to the surface under its own buoyancy, while recording pressure time series on four hydrophones with vertical and horizontal spacing over the bandwidth 5 Hz—30 kHz. The complete vertical noise profile was recorded from the surface to 6 km in the Philippine Sea, 8.5 km in the Tonga Trench, and 9 km in the Serena and Challenger Deeps in the Mariana Trench. Generally, the vertical noise coherence is well described by the Cron & Sherman surface noise model with some depth-dependence explained by seawater attenuation and local sound speed variations. Below the reciprocal or critical depth (depth at which the sound speed is higher than any sound speed above it), the total noise field statistics depend heavily on the relative contributions of locally generated surface noise and distantly generated propagating noise. Thus, the depth dependence of the sound field varies with local surface conditions, with nearly depth-independent noise power and coherence found at moderate states, while a sharp drop in noise level is found below the critical depth during calm sea states.
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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 | 0.000 |
| 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".