Quantifying ship noise in the marine soundscape of the western Canadian Arctic
Bibliographic record
Abstract
The Arctic soundscape is naturally quite complex, but many parts of the Arctic have also historically had very low levels of ship noise. However, ship traffic is increasing throughout the Arctic, which is likely leading to increased levels of underwater noise, causing changes in this soundscape. In this study, we thoroughly quantified ship noise in passive acoustic data collected at 10 sites in the western Canadian Arctic between 2014 and 2020, with data collected from between one and three years at each site. We paired the acoustic data with automatic identification system ship data to collect information on the individual ships creating noise. We quantified the presence of ship noise within all of the acoustic data, statistically examined the influence of different static and dynamic ship variables on sound levels, and estimated source levels of ships that traveled close to the acoustic recorder. These analyses represent the first detailed examination of ship noise in this region of the Arctic, and the results provide valuable information for future soundscape studies, as well as relevant information for the management of ship noise in the Arctic.
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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 | 0.004 |
| 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) | 0.001 | 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".