Localization and tracking performance of a stationary compact array of synchronized hydrophones
Bibliographic record
Abstract
Stationary compact arrays of synchronized hydrophones are an efficient tool to evaluate the azimuth and elevation angles and positions of marine mammals, vessels, and other sources that produce detectable sounds. A compact array designed by JASCO Applied Sciences (Canada) Ltd., was used to estimate the azimuth and elevation angles of ship noise and marine mammal calls and to estimate positions and to track sources from bearing only measurements provided by both single and multiple arrays. Array performance was tested with various sources that transmitted impulsive and continuous sounds; GPS coordinates were known for all sources. Bearing and position accuracy as functions of sound bandwidth, duration, and other parameters, were estimated. Test results demonstrated that the array’s accuracy came close to the Cramér-Rao bounds. In in situ tests, correlated bearing errors were observed. Refraction, surface and bottom reflections and other unpredictable sound propagation effects caused most of the bearing errors. The source position and heading angle estimation accuracy was evaluated using the array deployed in the Strait of Georgia, BC, Canada. Test results demonstrated that the array can provide the highest possible accuracy and can be used in various applications involving long-term passive acoustic monitoring of large areas.
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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".