The effect of submarine canyon bathymetry on range estimation using cross-correlated ship noise field
Bibliographic record
Abstract
Ambient noise data collected on a pair of omni-directional vertically separated hydrophones at the head of Alvin Canyon, a shelf-break submarine canyon, is used to estimate the range and bearing of a passing vessel. Comparison of the vertical coherence data against a Pekeris normal-mode model may be used to provide the range of the ship along bearing lines that do not cross the canyon’s rapidly changing bathymetry. To investigate the effect of bathymetry on the range estimate, a reciprocal three-dimensional Parabolic Equation (3-D PE) model is used to generate a map of the vertical coherence field for all possible ship positions over the domain of a Gaussian canyon, demonstrating that horizontal reflection and refraction lead to focusing of ship noise along the canyon axis. The same method is used to obtain vessel range and bearing information from the data recorded at Alvin Canyon. The vessel bearing relative to the pair of vertically spaced omni-directional receivers is obtained by exploiting the canyon bathymetry.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".