Long-term measurements of ambient sounds in Cambridge Bay (Canada), 2015-2024-Implications for extending the MSFD to Arctic waters
Bibliographic record
Abstract
Climate change in the Arctic enables increased access to human activities, affecting underwater soundscapes. It is therefore important to have complete guidelines to monitor impacts on natural environments. The EU Marine Strategy Framework Directive is the most complete, strongly inspiring emerging guidelines in other countries. Primary descriptor D11C2 addresses continuous low-frequency sounds and makes extensive use of third-octave “shipping bands” at 63 and 125 Hz. To address the lack of measurements, models often use ship tracks recorded by their Automatic Identification Systems (AIS). But not all ships in the Arctic use AIS, and winter ice also allows human activities other than shipping. We use sound measurements by Ocean Networks Canada in Cambridge Bay (Nunavut) between 2015 and 2024, focusing on the months of May (full ice cover, no shipping) and August (little to no ice, shipping activity). We show impacts beyond the “shipping bands”. Baseline soundscapes vary with ice cover and AIS underestimates impactful activities of all types. Our results show that future guidelines will need adapting to the Arctic environments to fully measure the range of human impacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".