Reductions in underwater radiated noise from shipping during the 2017 Haro Strait vessel slowdown trial
Bibliographic record
Abstract
During 2017, the Vancouver Fraser Port Authority's Enhancing Cetacean Habitat and Observation (ECHO) program carried out a voluntary slowdown trial in Haro Strait (British Columbia) to investigate whether limiting vessel speeds to 11 knots would decrease noise in Southern Resident Killer Whale habitat. During the trial, JASCO collected source levels measurements on two underwater listening stations situated adjacent to the Haro Strait traffic lanes, while a third listening station in Georgia Strait measured noise from vessels outside the slowdown zone. Acoustic data from these three listening stations were analyzed using JASCO's PortListen® system, which tracks vessels using the Automated Identification System (AIS) and automatically measures the source levels of passing vessels, according to the ANSI standard for ship noise measurement (12.64-2009 R2014). The effects of voluntary slowdowns on vessel noise emissions were investigated, on a per-class basis, by comparing measurements of participating vessels with measurements obtained during control periods before and after the trial. Analysis of the trial data showed that speed reductions were an effective method for reducing broadband source levels for five categories of piloted commercial vessels: containerships, cruise vessels, vehicle carriers, tankers, and bulk carriers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".