Underwater Noise from Large Commercial Ships—International Collaboration for Noise Reduction
Bibliographic record
Abstract
Abstract Ambient noise in broad areas of the ocean has increased significantly over the past half‐century from the introduction of tens of thousands of commercial ships continuously transiting the sea. Ship‐radiated noise is predominately low frequency (<1000 Hz) other than close to vessels, and aggregate noise can dominate low‐frequency bands, even well outside shipping lanes. Such sounds add to an already noisy background and can affect marine animals in various ways. This includes reducing the areas over which they can communicate, particularly for species that rely on low‐frequency sounds like baleen whales, seals, and fishes. An international community of researchers, environmental groups, government agencies, and sectors of the shipping industry has recognized shipping noise as an important marine conservation issue, as have various international bodies, notably the United Nation's International Maritime Organization (IMO). Reducing potential impacts from aggregate vessel noise is challenging given the nature and magnitude of the issue and the historical lack of regulation. However, substantial recent progress has been made by proactive collaborations among environmentalists, regulators, scientists, and industry, leading to progress in the IMO in the development of guidelines for the reduction of underwater noise from commercial shipping. This article discusses low‐frequency noise incidentally radiated from ships and its potential effects on marine life, with an emphasis on marine mammals. We also trace the formation and evolution of efforts to address environmental and economic costs and benefits of ship‐quieting efforts. The authors represent a range of governmental, scientific, industry, and conservation organizations centrally engaged in the IMO effort.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".