Quantifying vessel noise and acoustic habitat loss in marine soundscapes
Bibliographic record
Abstract
Quantifying underwater vessel noise in marine ecosystems is challenging, due to difficulties in accounting for small, not publicly tracked boats, creating a knowledge gap in marine management. We present a computationally efficient framework that detects all vessel noise in hydrophone recordings and quantifies associated excess noise levels as well as acoustic habitat loss, offering a cost-effective and replicable tool for assessing vessel noise effects on marine soundscapes. Applied to one year of acoustic data from five sites along the coast of British Columbia (BC), Canada, the detector achieved 96.4 % accuracy and was robust against varying levels of vessel traffic and weather conditions. Across sites, vessel noise impacts increased with proximity to urban centers. Following this trend, average annual vessel noise presence ranged between 24 % and 85 %, increasing the 500 Hz decidecade band by 1.0 dB to 6.4 dB across sites. The average year-round acoustic habitat loss for killer whales, expressed as the reduction of listening space in a 0.5-15 kHz communication band, ranged from 6.6 % to 46.9 %. Vessel noise impacts were generally higher during daylight hours and in the summer months. The results are the first comprehensive, empirical assessment of vessel presence and associated noise impacts for a regional ecosystem in BC.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".