Passive acoustic localization and density estimation of fin whales using a single hydrophone
Bibliographic record
Abstract
This paper presents an approach for carrying out passive underwater acoustic localization of a vocalizing marine mammal with data from a single receiver, and aims to build upon earlier work on localization based on the time of arrival of acoustic energy traveling either directly from source to receiver, or bouncing one or more times off the ocean surface and/or bottom. This localization approach will be applied to a multi-year data set consisting of thousands of fin whale vocalizations, necessitating an approach with a high degree of automation. This approach uses a comparison between the recorded fin whale vocalizations and model-predicted acoustic waveguide impulse responses at a set of candidate ranges and depths relative to the receiver. The location whose estimated impulse response best matches the recorded fin whale call is deemed to be the estimated source position. A small number of calls distributed throughout the data record were localized individually using a linearized Bayesian localization approach with a quantitative uncertainty analysis incorporating both data and environmental uncertainties. The data for this study were recorded by the single hydrophone in the ALOHA cabled observatory, located 100 km N of Oahu in 4728 m of water. Repeated hydrographic, chemical, and biological sampling has been done at this site since 1988, facilitating acoustic propagation modeling in the vicinity of the ALOHA site. The primary motivation for this work is to estimate the local population density of fin whales observed near the ALOHA cabled observatory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".