Ship speed reduction is an effective mitigation for underwater noise effects
Bibliographic record
Abstract
The Vancouver Fraser Port Authority’s ECHO Program led a voluntary 2-month ship slowdown trial during which 56% of piloted ships slowed down to <13 knots in a 16 nautical mile corridor. The goal was to determine if a slowdown could be used as a mitigation measure to reduce ship related underwater noise effects in core Southern Resident killer whale habitat. A calibrated hydrophone system was used to measure ambient noise levels from 10 Hz to 100 kHz during the trial and a representative 2-month baseline period. The hydrophone was located 2.3 (inbound) and 5 km (outbound) from the center of the shipping lanes at 23 m depth. Analyses of data with ships present showed a median broadband noise reduction of 2.5 dB. This reduction was highest in the 10–100 Hz decade band (3.1 dB) and lowest in the 10,000–100,000 Hz band (0.3 dB). A statistical model found that received noise levels were best described by the distance to ships, the presence of small boats, water velocity, slowdown period, ship speed through water, and wind speed. This highlights that appropriate temporal scales and the inclusion of covariate data are needed to adequately measure ship related changes in underwater ambient noise levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".