Can we be confident about ship noise measurements?
Bibliographic record
Abstract
Increased maritime traffic is leading to more underwater noise. Despite the use of a standardized protocol (ANSI/ASA S12/64-2009), measuring ship noise is complex, as it is difficult to follow the protocol in real-life conditions. The Marine Acoustic Research Station (MARS) project (www.projet-mars.ca/en) designed a recording station and a measurement protocol that follows closely the international standard. The station operated during summer/fall in 2021, 2022 and 2023 in the Laurentian Channel in the St. Lawrence Estuary (eastern Canada). Four dedicated missions were organized, during which the R/V Coriolis II was measured repeatedly, generating a database of 239 measurements. In this study, we assess the measurement repeatability of the MARS station, comparing simultaneous measurements from identical systems, as well as repeated measurements at varying speeds and distances. We quantify the measurement uncertainties associated to the hydrophone configuration and the measurement protocol. Our results illustrate how array redundancy allows to identify and mitigate hardware-related issues and to reduce the duration of the measurement, a costly parameter. We quantify the repeatability of single measurements within the MARS station, we discuss the implications regarding our ability to validate noise reduction actions from ~3 dB and we propose a measurement protocol to further reduce this limit.
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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 | 0.078 | 0.393 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.032 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".