Ranking vessel noise emissions using measurements from an underwater listening station
Bibliographic record
Abstract
Commercial shipping routes pass through important habitat areas for several species of marine mammals in the coastal waterways of southern British Columbia. The Vancouver Fraser Port Authority, through its Enhancing Cetacean Habitat and Observation (ECHO) program, has undertaken studies to develop mitigation measures that will lead to a quantifiable reduction in threats to whales resulting from shipping activities. This includes long-term measurements of vessel noise at a cabled underwater listening station in Georgia Strait (the ECHO ULS) where JASCO, in partnership with Ocean Networks Canada, has been measuring source levels of vessels calling at the Port using JASCO's PortListen® software. PortListen® receives and processes real-time acoustic and AIS data to calculate vessel source levels using ANSI standard methods (S12.64-2009 R2014). Since September 2015, PortListen® has collected a database of thousands of source level measurements, which has been used to implement ranking system for vessel noise emissions. The ranking system uses a data-driven model to adjust the ranking of each measurement according to the vessel characteristics (e.g., size, class) and measurement conditions (e.g., speed, wind, and draft). In addition to an unweighted noise ranking, the system also provides weighted rankings for five marine mammals hearing groups using NOAA (2016) auditory weighting curves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".