The SOUNDS project: towards effective mitigation of underwater noise from shipping in Europe
Bibliographic record
Abstract
Continuous underwater noise from shipping has been identified as one of the main contributors to ambient noise levels in the oceans. Notwithstanding the potential impacts on marine life, the subject started receiving attention from international and regional regulatory bodies only very recently. Last year, the European Maritime Safety Agency commissioned a study to consolidate information about continuous underwater noise from shipping in order to derive recommendations for a future multi-stakeholder strategy within Europe. The work reviewed information about sources of continuous noise, environmental impacts, the policies in place to manage underwater noise and available mitigation measures to reduce noise levels. Effective management of underwater noise from ships is a multi-sectoral challenge requiring coordination between different policies and stakeholders. Based on an online survey and a number of interviews, it was possible to elucidate: 1) how different stakeholders interact; 2) the main drivers for addressing underwater noise; and 3) possible strategies for tackling the subject effectively. Additionally, considerations and lessons learned from ECHO program in Canada were analysed as a case study. The paper will describe the main activities carried out in the project, focusing on recommendations for effective mitigation of ship noise.
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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 | 0.005 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".