A quantitative soundscape analysis of the Canadian Arctic, 2014–2019
Bibliographic record
Abstract
In the Arctic, sound levels have historically been strongly tied to sea ice and wind speed, with very little impact of anthropogenic noise. However, climate change is causing a loss of sea ice, and consequently increased ship traffic and anthropogenic underwater noise. Here, we present the first quantitative, comparative analysis of underwater sound levels across the Canadian Arctic. We analyzed 39 passive acoustic datasets collected throughout the Canadian Arctic from 2014 to 2019 to examine spatial and temporal trends in sound pressure levels (SPL), quantify environment drivers of SPL, and estimate the influence of ship traffic on SPL. Daily mean SPL in the 50–1000 Hz bandwidth ranged from 70 to 127 dB re 1 μPa (median = 91 dB). SPL increased as wind speed increased, but decreased as both ice concentration and air temperature increased. The highest SPLs were in August-October, and the lowest in March–April. SPL increased as the number of ships increased. The highest mean SPLs were recorded near southeast Baffin Island, but the most ship noise was recorded near Pond Inlet (>1 ship/day in summer). This study provides an important baseline for underwater sound levels in the Canadian Arctic, and fills many geographic gaps on published underwater sound levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".