Enabling long-term soundscape observation via Ocean Networks Canada’s acoustic Northeast-Pacific infrastructure
Bibliographic record
Abstract
Data time series covering extended periods of time in support of long-term environmental studies are particularly difficult and expensive to collect, especially at offshore locations. Ocean Networks Canada’s (ONC) undersea cabled observatories in the Northeast Pacific ocean have been collecting an extensive array of oceanographic, seismic, geophysical, biological and acoustical data over periods that, in some cases, extend beyond sixteen years. With regard to soundscape studies, ONC currently owns and operates 22 hydrophones (including four 4-element, three-dimensional arrays) between the VENUS coastal observatory in the Salish Sea and the NEPTUNE offshore deep-sea observatory in the Northeast Pacific Ocean. The data, streamed in quasi-real-time to ONC’s web data portal, offer a window on a number of different environments, from the busy, shallow waters of the Salish Sea to the 2200 m of depth of the Endeavour hydrothermal-vent field. This presentation gathers highlights from the latest research utilizing ONC’s acoustic infrastructure and data, ranging from ambient noise to hydrothermal-vent soundscapes, seismic events, bioacoustics, and signal processing applications such as source localization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".