Influence of vessel disturbance on Pacific harbour porpoise (<i>Phocoena phocoena vomerina</i>) echolocation
Bibliographic record
Abstract
Vessel disturbance is one of many anthropogenic threats that are negatively impacting coastal cetacean populations worldwide. Noise pollution from vessels can cause varying levels of disturbance in cetaceans, depending on several factors such as vessel type and speed. Pacific harbour porpoises (Phocoena phocoena vomerina) are distributed throughout coastal waters of the North Pacific Ocean, with large aggregations observed near the entrance to the Port of Prince Rupert in British Columbia, Canada. This area serves as an important year-round foraging ground for harbour porpoises. However, it is also one of the fastest growing container ports in North America, with planned increases in activity. Harbour porpoises are highly sensitive to vessel-related acoustic disturbances, but the effects of vessel activity on their foraging rates remain unclear. In this study, we used a combination of land-based surveys, passive acoustic monitoring (PAM) devices (C-PODs and F-PODs), and automatic identification system (AIS) data to investigate the relationship between vessel activity and harbour porpoise echolocation activity-both foraging and non-foraging-in the heavily trafficked Chatham Sound, adjacent to the Port. Our results show that an increase in the total number of vessels negatively affected both foraging and non-foraging echolocation activity, with less echolocation observed in the presence of more ferries and tugs. Similarly, vessels traveling at higher speeds (>6 m/s kn) had a negative effect on echolocation activity. Tugboats and passenger vessels, in particular, had a wider range of effects on all harbour porpoise echolocation activity. Our findings indicate that implementing a vessel slowdown (~5 m/s) along the approach to the Port of Prince Rupert would reduce disturbances to harbour porpoises and likely benefit other coexisting species that rely on quiet oceans for communication and foraging.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".