Shallow Water Low-Frequency Propagation Loss Predictions and Measurements Towards Acoustic Range Characterization
Bibliographic record
Abstract
Low-frequency ship noise negatively impacts marine ecosystems and interferes with marine mammal communications. In an effort to manage and reduce ship noise in marine environments, high-fidelity propagation loss measurements were conducted across multiple locations to develop a principled shallow water ranging methodology that improves the accuracy of shallow water ship ranging. This paper reports on a proposed propagation loss procedure and presents preliminary results from in-water tests. Calibrated acoustic projectors, driven by a programmable function generator, were used to simulate different types of ship noise. A surface-mounted, near-field hydrophone, and a pair of bottom-mounted, far-field hydrophones were used to receive the acoustic signals. Propagation loss (PL) was estimated from the near-field and far-field hydrophones to yield source level (SL) estimates. These SL estimates were compared with the calibrated acoustic projector's known source levels to verify the propagation loss accuracy. Future work will further develop PL estimation, resulting in a shallow water ranging technique with a performance comparable to that of deep water ranging.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".