Using vessel noise from a single hydrophone to estimate environmental properties
Bibliographic record
Abstract
This paper estimates seabed and water-column properties of a shallow-water site in the Chukchi Sea using vessel noise recorded on a single ocean-bottom hydrophone. A shallow-hazards seismic survey vessel transited with a fixed heading, passing within 200 m of the hydrophone. Sound pressure levels as a function of frequency and range are inverted using a trans-dimensional (trans-D) Bayesian approach to estimate range-independent environmental properties (sound-speed profile, water depth, and seabed geoacoustic properties). The trans-D inversion allows the data to determine the most appropriate environmental model parameterization in terms of the number of sound-speed profile nodes and subbottom layers. The inversion also estimates the vessel source levels, source depth, hydrophone height above the seabed, a range-correction factor, and error statistics, and provides uncertainty estimates for all model parameters and parameterizations. The sound-speed profile is found to be in good agreement with a measured profile, and the upper sediment-layer sound speed agrees with estimates from separate inversions of airgun and bowhead whale modal dispersion data. This method could be applied to monitor sound-speed profile changes over long time scales, e.g., using Automatic Identification System position data for vessels in a shipping lane.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".