Array element localization using ship noise
Bibliographic record
Abstract
This paper describes a method of estimating hydrophone positions in a receiver array using the noise from a passing ship. Relative arrival times of the ship-noise signal between pairs of hydrophones are obtained from several time windows of data (corresponding to different ship locations) by cross-correlating the band-pass filtered time series. The relative arrival times are used as data in an array element localization inversion to estimate both the hydrophone and ship locations based on iterated linearization of the acoustic ray equations. The inversion applies the method of regularization to include prior information such as approximate location estimates and uncertainties for the source and receivers and the expectation that the array shape and or source tracks are smooth functions of position. Linearized and nonlinear (Monte Carlo) estimates of the position errors are in good agreement and indicate a high degree of confidence in the receiver positions (relative uncertainties of approximately 0.2 m in the horizontal and 0.05-0.1 m in the vertical). The ability to improve upon the initial source position estimates depends on the geometry of the problem, as investigated with simulations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".