Bibliographic record
Abstract
The impact on marine mammals of underwater radiated noise (URN) from vessel traffic is under active international study and of increasing concern. The focus has been on “passive” URN from machinery, hull, propulsion, and flow noise. Active acoustic radiation from echolocation devices has received little attention, although these devices are pervasive, mandated for a wide range of vessels and contribute significantly to the overall URN signature within the most sensitive portions of the hearing ranges of some threatened whale species. It is concluded that a highly effective mitigation measure, in step with market trends, would be a move from traditional low frequency devices to 200+ kHz operation with broadband (chirp, typically) waveforms, a high degree of power control and the ability to temporarily deactivate lower frequency channels in the case of multi-frequency devices. The most rapid benefits can be achieved by encouraging this in the unregulated sector comprising recreational vessels and vessels with displacement less than 150 GT. The regulated sector above 150 GT can also benefit over a longer time scale, due primarily to type-approval requirements and associated schedules and costs. Recommendations are provided for a way ahead, including a program of further interaction with manufacturers and users to highlight these issues and further develop the operational and technical solutions in a manner that is quickly and easily deployed across all fleets.
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 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 | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.572 | 0.331 |
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".