A comparison of quiet ship certifications with the Enhancing Cetacean Habitat Observation (ECHO) ship noise database
Bibliographic record
Abstract
Measurements from the large ship noise database acquired by the Vancouver Fraser Port Authority’s Enhancing Cetacean Habitat Observation (ECHO) program were used to assess the conservativeness of five vessel noise certification societies. A multi-variate linear regression analysis of the database was used to scale ECHO measurements to a common reference vessel type for each of 6 categories: tug, tanker, bulker, container ship, vehicle carrier, cruise ship. The purpose of scaling the ECHO measurements was to create a modified dataset to compare with existing vessel noise certification society noise thresholds. The conservativeness of the certification society thresholds was found to vary with vessel category. The general findings are that the society limits are conservative for faster categories (e.g., container ship) but not for slower vessels such as tankers, and certification systems using monopole source level (MSL) had better matches with measurement data than the approaches using radiated noise level (RNL). None of the certification societies accounts for differences of vessels within a vessel category. Therefore, small ships are currently evaluated against the same threshold criteria as large ships. The scaling system developed here using the ECHO dataset could be used to scale measurements (or thresholds) to account for different vessel sizes and operating conditions. This research was funded by Transport Canada.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".