Robust 2D localization of low-frequency calls in shallow waters using modal propagation modelling
Bibliographic record
Abstract
We propose a new method to localize low-frequency calls in 2D in shallow waters from a sparse array of hydrophones using modal propagation modelling.An analysis of modal propagation modelling of transients signals in shallow water environment shows that the dispersive behaviour of the waveguide can be exploited to design a robust localization scheme without requiring any knowledge of the acoustics properties of the environment (bottom and water column) nor any simulation of propagation.The localization scheme also does not require synchronization of the array and is therefore independent of any clock drift.Promising results are obtained for Northern right whale gunshot calls from 'Bay of Fundy data set of the 2003 Workshop on Detection and Localization of Marine Mammals Using Passive Acoustics.'r s u m Dans ce papier, un algorithme robuste de localisation 2D partir des missions transitoires dans des milieux petits fonds est propos.Il s'appuie sur un modle de propagation modale.Une analyse des phnomnes de dispersion induits par la propagation montre qu'il est possible, partir d'un rseau lche d'hydrophones, de proposer une mthode de localisation ne ncessitant ni la connaissance du milieu, ni l'excution d'un code de propagation.L 'algorithme de localization ne ncessite pas la synchronization du rseau et est par consquent indpendant des drives d'horloges.Des rsultats encourageants sont obtenus pour localiser les missions gunshot des baleines franches partir du jeu de donnes de la Baie de Fundy, de 'l'Atelier de 2003 sur la dtection, la localisation et la classification de mammifres marins par acoustique passive'.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".