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Record W4312378355 · doi:10.1121/2.0001638

The SOUNDS project: towards effective mitigation of underwater noise from shipping in Europe

2022· article· en· W4312378355 on OpenAlexaboutno aff
Erica Cruz, Thomas Lloyd, Frans Hendrik Lafeber, Johan Bosschers, Guilherme Vaz, Samy Djavidnia

Bibliographic record

VenueProceedings of meetings on acoustics · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsNoise (video)UnderwaterAgency (philosophy)StakeholderComputer scienceNoise controlEnvironmental resource managementBusinessEnvironmental scienceOceanographyNoise reductionPolitical scienceArtificial intelligencePublic relations

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.232
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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