Impact of ship noise on the underwater soundscape of Eclipse Sound in the northeastern Canadian Arctic
Bibliographic record
Abstract
Eclipse Sound, in the northeastern Canadian Arctic, has experienced a substantial increase in ship traffic due to growing tourism and industrial development in the region. This study aims to describe the natural soundscape as well as to assess the noise levels associated with shipping. Underwater sound recordings were collected at two locations: Eastern Eclipse Sound (72° 43.730 N, 76° 13.519 W, 670 m) leading to Baffin Bay, and Milne Inlet (72° 15.260 N, 80° 34.205 W, 313 m) situated near the southwest end of Eclipse Sound. To capture the dynamic nature of the soundscape, the data from these two locations were divided into three seasons: late spring, summer, and early fall. These periods were selected to account for the changing contribution of sea ice to the soundscape during the sea ice break-up, two months of open water, and the sea ice freeze-up. By analyzing ship tracks and underwater acoustic recordings, we identified patterns of ship traffic and estimated underwater noise levels due to ships. Noise emitted by ships is quantified by vessel type, including three cargo ship types, passenger ships, pleasure craft, and icebreakers. Individual ship transits through the region introduce transient noise at frequencies from <20 Hz to >20 kHz, with durations lasting from a few minutes to >6 h. The impact of ship noise on the soundscape is significant, resulting in increases in sound levels by 15 to >30 dB when ships are within 10 km and measurable ship noise below 200 Hz at distances of >50 km.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".