Slowing deep-sea commercial vessels reduces underwater radiated noise
Bibliographic record
Abstract
During 2017, the Vancouver Fraser Port Authority's Enhancing Cetacean Habitat and Observation program carried out a two-month voluntary vessel slowdown trial to determine whether slowing to 11 knots was an effective method for reducing underwater radiated vessel noise. The trial was carried out in Haro Strait, British Columbia, in critical habitat of endangered southern resident killer whales. During the trial, vessel noise measurements were collected next to shipping lanes on two hydrophones inside the Haro Strait slowdown zone, while a third hydrophone in Strait of Georgia measured vessels noise outside the slowdown zone. Vessel movements were tracked using the automated identification system (AIS), and vessel pilots logged slowdown participation information for each transit. An automated data processing system analyzed acoustical and AIS data from the three hydrophone stations to calculate radiated noise levels and monopole source levels (SLs) of passing vessels. Comparing measurements of vessels participating in the trial with measurements from control periods before and after the trial showed that slowing down was an effective method for reducing mean broadband SLs for five categories of piloted commercial vessels: containerships (11.5 dB), cruise vessels (10.5 dB), vehicle carriers (9.3 dB), tankers (6.1 dB), and bulkers (5.9 dB).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".