Underwater Noise Measurement Station for Vessels in the Salish Sea
Bibliographic record
Abstract
A collaborative project between Port Metro Vancouver, Transport Canada, Ocean Networks Canada (ONC) and JASCO Applied Sciences (Canada) Ltd., has installed an underwater listening station along the northbound (incoming) shipping route to Burrard Inlet. This system is designed to characterize the acoustic emissions of large numbers of vessels that transit a predefined source measurement track. The results are important for assessing marine fauna exposures to noise throughout the Salish Sea and for designing possible vessel noise mitigation strategies. The underwater listening station consists of two AMAR Observer systems from JASCO Applied Sciences, connected to ONC’s VENUS East Node underwater observatory. Each AMAR Observer can accurately track vessels and simultaneously measure vessel sound levels using tetrahedral hydrophone arrays. Further vessel tracking information is provided by a dedicated vessel Automatic Identification System (AIS) installed by ONC. Acoustic data are digitized at 64 kHz on all 8 hydrophone channels, producing a large amount of data (1.5 MB per second). ONC’s East Node also collects salinity, temperature, and water current data. All acoustic and oceanographic data are transmitted in real-time over the VENUS network to ONC’s shore-based storage and high-performance-computer processing systems at University of Victoria. There, JASCO’s automated acoustic software analyzes the data and produces source level measurement reports for each vessel pass. The acoustic range has additional capabilities, including the automatic detection of marine mammal calls and measurement of ambient noise levels. In this presentation we will outline the technical design of the underwater listening station and we will discuss the purpose of these measurements and their relevance for assessing vessel noise in the Salish Sea.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".