Developing and testing systems for the attenuation of ships' machinery noise
Bibliographic record
Abstract
With ships operating in areas including important marine biodiversity, it is crucial to understand the sources and characteristics of underwater noise and develop effective measures to mitigate underwater noise's impact on the environment. The underwater noise signature from a ship is usually dominated by machinery noise and propulsion engines at low speeds. Given the importance of onboard electrical power, diesel generators might continuously function (i.e., even when a ship is docked) and generate low-frequency underwater noise. Université de Sherbrooke (Sherbrooke, Canada) and Innovation Maritime (Rimouski, Canada) lead a collaborative work focusing on the means and methods for attenuating machinery noise. Two main research axes are considered. The first concerns the setup and validation of a small-scale platform that can be used to test noise reduction methods in a controlled water basin environment. The second research axis investigates passive, tunable, and possibly multi-resonant vibroacoustic solutions to attenuate machinery noise. These solutions mostly come as 3D-printed folded quarter-wave resonators following spiral or cantilever resonators. Acoustic resonators can also be embedded into a layer of sound-absorbing material to improve soundproofing properties at specific frequencies. The applications and scope of this work are finally put into perspective.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".