Passive acoustic tomography: new concepts and applications using marine mammals: a review
Bibliographic record
Abstract
This paper presents the new concept of passive acoustic tomography which allows ocean data collection with a passive acoustic remote sensing process. The originality lies in using acoustic sources of opportunity such as surface noise, radiated ship noise and marine mammal calls. Such use of passive tomography is a promising way to reduce acoustic emissions in oceans. A review is first presented, including the description of new concepts of covert active, assisted passive and autonomous tomography, followed by applications on real world data. Under the assumptions of multipath propagation and measurements performed by a sparse network of hydrophones, a time–frequency processor is proposed to simultaneously estimate the source location and the impulse response of the propagation channel for marine mammal calls used as opportunistic sources (multipath structure, time delay and attenuation are estimated). Promising results are obtained on real data coming from the Laurentian channel where wideband beluga calls (1 to 3 kHz) are measured by a sparse network of 6 bottom hydrophones.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".