Real-Time Monitoring of Coastal & Offshore Construction Noise for Immediate Decision Making
Bibliographic record
Abstract
Abstract With the global awareness of the need to make our energies cleaner, marine constructions, typically wind farms, and especially offshore, have multiplied in recent years, and we can expect to see these numbers increase even more rapidly. The presence of marine mammals during offshore infrastructure works (pile driving, drilling, dredging) is now a major environmental concern, as it has been proven that they could be severely harmed by exceeding noises. In order to safeguard species and their natural habitats, more and more local legislations impose a cap on sound levels caused by all offshore activities. As of 2023, this is the mainly the case in Europe (for instance in the United Kingdom [Joint Nature Conservation Committee - JNCC, Southall et al., 2007; Popper and Hasting, 2009], Germany [Bundesamt fur Seeschifffahrt und Hydrographie - BSH (Federal Maritime and Hydrographic Agency), Muller-BBM, 2011], The Netherlands [Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek - TNO (Netherlands Organisation for Applied Scientific Research), 2011], Belgium or Denmark [Danish Energy Agency - DEA, Tougaard et al., 2016]), but Asian countries (Taiwan [Environmental Protection Administration - EPA, 2019] being the best example) and American (USA, Canada) are also implementing similar rules. Wind farms developers therefore are required to measure, monitor, and mitigate noise caused by building work, and, today, underwater noise monitoring regulations are enforced as a means of protecting aquatic life.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".