A Two-Stage Method for Determining the Position and Corresponding Precision of Marine Mammal Sounds; 2004BU1-OT
Bibliographic record
Abstract
Today there is a concern that man-made sounds, such as that from sonar experiments, seismic operations and oil rigs, affect marine mammals. Detection and localisation of marine mammals will definitely support measures to reduce the possible detrimental effects. This paper presents a two-stage localisation method, which is applied to data collected with an array of five hydrophones moored to the seabed in the Bay of Fundy, Canada. The array forms a 14 by 14 km square with one hydrophone in the centre. The method makes use of the relative travel times of the mammal's sound to the four hydrophones at the square vertices with respect to the travel time to the central hydrophone. First, a good initial position is obtained using hyperbolic fixing. In the second step the solution is improved in an iterative process, where each iteration determines the least-squares solution of the set of four linearized equations for the measured relative travel times. Calculating the error ellipse from the covariance matrix of the solution provides the localisation accuracy. There are several parameters that affect the source position accuracy. These include the uncertainties in arrival times, sound speed and receiver positions. Their effect on the localisation accuracy is assessed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".