Quantifying the contribution of ship noise to the underwater sound field
Bibliographic record
Abstract
The ambient sound field in the ocean can be decomposed into a linear combination of two independent fields attributable to wind-generated wave action at the surface and noise radiated by ships. The vertical coherence (the cross-spectrum normalized by the power spectra) and normalized directionality of wind-generated noise in the ocean are stationary in time, do not vary with source strength and spectral characteristics, and depend primarily on the local sound speed and the geoacoustic properties which define the propagation environment. The contribution to the noise coherence due to passing vessels depends on the range between the source and receiver, the propagation environment, and the effective bandwidth of the characteristic source spectrum. Using noise coherence models for both types of the sources, an inversion scheme is developed for the relative and absolute contribution of frequency dependent ship noise to the total sound field. A month-long continuous ambient sound recording collected on a pair of vertically aligned hydrophones near Alvin Canyon at the New England shelf break is decomposed into time-dependent ship noise and wind-driven noise power spectra. The processing technique can be used to quantify the impact of human activity on the sound field above the natural dynamic background noise, or to eliminate ship noise from a passive acoustic monitoring data set.
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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) | 0.000 | 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 | 0.000 |
| 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".