Probabilistic Estimation of Merchant Ship Source Levels and Seabed Geoacoustic Profiles in a Shallow-Water Environment
Bibliographic record
Abstract
The estimation of ship source levels (SSL) in shallow-water environments is affected by sound interaction with the seabed.Uncertainty in seabed properties influence SSL estimates, and it is of interest to mitigate and quantify such effects.This paper applies a probabilistic approach to ship radiated noise recorded on hydrophone line arrays to infer SSLs and properties of a mud-sand seabed in shallow waters (depths ~80 m) on the New England Shelf.The approach, trans-dimensional Bayesian marginalization, samples probabilistically over source strengths, source depths/ranges, number of seabed layers and geoacoustic parameters of each layer.Radiated noise due to three large merchant ships passing at 2-4 km range from hydrophone arrays is considered.The estimated SSL spectra agree well with reference spectra.The average SSL uncertainty is 3.2 dB/Hz for narrowband (20-120 Hz) noise and 1.8 dB/Hz for broadband noise .Seabed layering and geoacoustic parameter estimates agree reasonably well with mud-over-sand seabed models from other inversions in the experiment area.
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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 | 0.000 | 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".