Arctic Marine Soundscape in Cambridge Bay, Nunavut, Canada, 2014 and 2024
Bibliographic record
Abstract
Ocean Networks Canada operates and maintains innovative cabled observatories that supply continuous power and Internet connectivity to various scientific instruments located in coastal, deep-ocean, and Arctic environments. This data set contains 5-minute audio files (n = 13,516) from Cambridge Bay, Nunavut, Canada collected in February 2014 and 2024 using Ocean Sonic icListen underwater hydrophones. Cambridge Bay is located along the southern portion of Victoria Island in the Canadian Arctic, and the Cambridge Bay Coastal Community Observatory is located 0.5 km from shore with the underwater hydrophone located at 13 m depth. Audio files were collected in 5-minute subsections and recorded continuously at 64 kHz sampling rate and 24-bit rate. Audio files were used to understand the changing under-ice marine soundscape over the last decade. The soundscape code was used to document changes related to amplitude, impulsiveness, periodicity and uniformity of the soundscape over time. All files were processed in MATLAB using the SSC metric tool for three frequency bands: the broadband range of the hydrophone (up to 32 kHz), hearing range of Arctic cod (< 1000 Hz), and the frequency band corresponding to Arctic cod grunts (50-500 Hz). Additionally, a subset of files were manually annotated in Raven Pro to examine the contribution of biological (e.g., Arctic cod grunts), geological (e.g., ice noise) and human generated noise sources (anthropogenic, e.g., snowmobile) to the soundscape.
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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 | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".