Changes in Underwater Noise and Vessel Traffic in the Approaches to Halifax Harbor, Nova Scotia, Canada
Bibliographic record
Abstract
Over the past two years, researchers at Fisheries and Oceans Canada have been running an acoustic monitoring project at multiple study sites throughout Nova Scotia, Canada to investigate baleen whale presence and levels of underwater noise. At the onset of the COVID-19 pandemic, a passive acoustic monitor (PAM) was in place in the study site located in the approaches to Halifax Harbor, a major Canadian port. This provided a unique opportunity to determine if changes in vessel noise levels occurred after pandemic restrictions were put in place. To investigate this, we analyzed and compared acoustic data collected from March 28 to April 28 and August 6 to October 22 in both 2019 and 2020. We also investigated possible changes in vessel traffic from February 1 through April 28 and July 1 through July 28 in 2019 and 2020 using terrestrial-based automatic identification system (AIS) data provided by the Canadian Coast Guard and cargo information provided by the Port of Halifax. The acoustic data were analyzed in 1/3 octave frequency bands. For the 89.1–112 Hz frequency band, we found an 8.4 dB increase in the daily minimum sound pressure level (SPL) in April 2020 compared to April 2019 due the presence of a large crane vessel stationed near the mooring site. For the period of August to October, we found an approximately 1.7 dB reduction in the same metric from 2019 to 2020. The most noticeable change in vessel composition was the dramatic decrease in the number and occurrence of pleasure craft in July 2020 compared to the same period in 2019. While this analysis looked at only a single PAM and a limited amount of data, we observed changes in sound levels in the frequency band known to be associated with shipping as well as changes in vessel traffic; we conclude that these observed changes may be related to pandemic restrictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".