Advancing glider-based acoustic measurements of underwater-radiated ship noise
Bibliographic record
Abstract
Ocean gliders are versatile and efficient passive acoustic monitoring platforms in remote marine environments, but few studies have examined their potential to monitor ship underwater noise. This study investigates a Slocum glider's capability to assess ship noise compared to the ability of fixed observers. Trials were conducted in shallow coastal inlets and deep bays in Newfoundland, Canada, using a glider, hydrophone array, and single-moored system. The study focused on (1) the glider's self-noise signature, (2) range-depth-dependent propagation loss (PL) models, and (3) identifying the location of the vessel to the glider using glider acoustic measurements. The primary contributors to the glider's self-noise were the buoyancy pump and rudder. The pitch-motor noise coincided with the buoyancy pump activation and did not contribute to the glider self-noise in our experiments. PL models showed that seafloor bathymetry and sound speed profiles significantly impacted estimates compared to models assuming flat and range-independent profiles. The glider's performance in recording ship noise was superior to that of other platforms. Using its hydrophones, the glider could identify the bearing from the vessel, although a third hydrophone would improve reliability and provide range. The findings demonstrate that gliders can characterize noise and enhance our understanding of ocean sound sources.
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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".