Analysis of the Variability of Ship Acoustic Signatures Measured as a Function of Hydrophone Configuration
Bibliographic record
Abstract
The intensification of maritime traffic implies an increase in underwater anthropogenic noise pollution. It is necessary to describe and quantify the noise generated by maritime traffic, in particular to assess the effects of ship noise on marine animals. Measuring underwater noise radiated by ships is complex, and strongly influenced by measurement conditions. A protocol has been developed to standardize methods for measuring and calculating ship-generated underwater noise (ANSI/ASA S12/64-2009). However, it is often not possible to comply with its many constraints, and each modification is likely to add uncertainty to the final measurement.The Marine Acoustic Research Station (MARS) applied research project (www.projet-mars.ca) is dedicated to understanding and measuring the underwater noise radiated by ships, and proposing appropriate methods for its reduction. An acoustic measurement platform is deployed every year in the Laurentian Channel in the St. Lawrence Estuary, designed to measure the acoustic signatures of ships as closely as possible to the international standard ANSI/ASA S12/64-2009.Since the station's first deployment in June 2021, 101 partner ship passes have been collected, as well as three specially dedicated missions during which the oceanographic research vessel Coriolis II made repeated passes at different distances and speeds. A total of 117 passages on the various measurement antennas have been recorded in 2022. In this way, it will be possible to study the uncertainties and errors in the measurement of the Coriolis II signatures as a function of speed, distance and the number of hydrophones, and to assess the measurement capability of a system as close as possible to the standard by looking at the variability of a ship's signature between antennas of identical configuration.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".