The greatest challenge for URN reduction in the oceans by means of engineering
Bibliographic record
Abstract
The Underwater Radiated Noise (URN) of the oceans due to shipping activity has increased during the last decades. This significant increment of URN can be explained by the remarkable growth of maritime traffic in recent years and the greater number of seismic explorations in the oceans. In relation to this issue, the current elevated level of underwater radiated noise in the oceans is producing a negative impact on marine fauna as it is happening with the Southern Resident Killer Whale (SRKW) in the Salish Sea in the west border between Canada and the USA. In the short-term, these high levels of URN block animal's ability to communicate, navigate, reproduce or hunt; while in the long-term, it may cause hearing loss in them. Hence, it is urgent to control the elevated level of URN in the oceans to protect the marine fauna and ensuring the sustainability of the sea environment. The majority of the underwater radiated noise produced by ships in the oceans originates from propellers, engines and auxiliary machinery. The International Council for the Exploration of the Sea (ICES) Report No209 was created with the aim to establish certain limit values on the URN generated by ships. The main purpose of this analysis is to avoid the negative effects of the URN on the sea environment and protect to the marine fauna of the vessel echosounders interferences focusing on the design and build of silent ships by means of a noise and vibration comprehensive management methodology. The scientific community has demonstrated that the anthropogenic noise produced by the shipping sector is the main responsible for the relevant growth of the URN in the oceans which causes negative effects on marine life [2]. As a result, a benchmarking study has been addressed by Canada to inform about the negative impacts of URN in the marine environment and propose the creation of new regulatory policies on URN reduction. This exploratory study has been carried out by a specialized steering committee which is composed of a network of organizations with a proven record in R&D and innovation activities in shipping URN. This technical study is in line with IMO conclusions for the reduction of URN from commercial shipping to address adverse impacts on marine life (MEPC.1/Circ.833; MEPC 74/17/2; MEPC 74/INF.36) representing the greatest challenge for the protection of the marine fauna in the oceans. To deal with the challenge of URN reduction in the oceans, the technological experiences accumulated over the years in the dynamic and acoustical design of modern Fishing Research Vessels (FRV) and Oceanographic Research Vessels (ORV) can be extrapolated to the rest of marine sectors. The main objective of this study is to identify which type of actions can be potentially used to manage and minimize URN generated by ships in the oceans without affecting the marine traffic and the transport of goods. The main idea is to design a new generation of “Silent Ships” in compliance not only with the strictest Comfort Class Notation requirements but also with the most restrictive URN requirements such as ICES No209. The overall URN level of a ship at certain operational speed is the combination of the noise spectral component generated by different URN sources such as the main engine and the propeller. Consequently, the reduction of the overall URN level will imply preventive actions for each one of the dominant URN sources. In summary, it can be concluded that a drastic reduction of the underwater radiated noise in the oceans is of vital importance to guarantee the sustainability of the sea environment and protect marine life. Thus, it is necessary to promote the design of a new generation of silent vessels using the most advanced engineering technology developed for the minimization of URN.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".