Beluga Vocalizations Decrease in Response to Vessel Traffic in the Mackenzie River Estuary
Bibliographic record
Abstract
Vessel traffic negatively affects marine mammals by causing behavioural disturbance, acoustic masking, contamination (i.e., oil spills), and ship strikes. Few studies have examined the effects of vessels on marine mammals in the Arctic, but beluga whales appear to be especially sensitive to vessel traffic. We examine how the vocalizations of belugas are impacted by vessel traffic in the Tarium Niryutait Marine Protected Area in the Mackenzie River estuary of the western Canadian Arctic. Between one and four acoustic recorders were deployed between June and August each year between 2015 and 2018 near the only shipping channel at this site. We examined beluga vocalizations from acoustic recordings over four summers and assessed how the distance to the nearest vessel passing the acoustic recorder affected the number of vocalizations. Beluga vocalizations within the range of the acoustic recorder decreased significantly when vessels were within 5 km of the acoustic recorder. This result suggests either that belugas are avoiding the vessel or that they reduce their vocalization in response to vessel traffic. Future work is needed to assess exactly how belugas are reacting to vessel traffic in this area and what the long-term consequences of these reactions are. Management measures for reducing these impacts must be carefully considered, especially since these vessels are very restricted in where they can travel, and many of the vessels are necessary for the livelihoods of local communities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".