The masking of beluga whale (<i>Delphinapterus leucas</i>) sounds by icebreaker noise in the Arctic
Bibliographic record
Abstract
Beluga whales are an Arctic and subarctic cetacean, with an overall “near threatened” conservation status, yet some populations are considered endangered. Apart from threats such as whaling, predation, contamination, and pathogens, underwater noise is of increasing concern. In the early 1990s, Fisheries & Oceans Canada started to fund research on underwater noise emitted by icebreakers and its bioacoustic impacts. In collaboration with the Vancouver Aquarium, beluga whales were trained for masked hearing experiments. Apart from measuring pure-tone audiograms in quiet conditions, animals were trained to listen for beluga vocalizations in different types of noise, including artificially created white noise, naturally occurring thermal ice-cracking noise, and an icebreaker’s propeller cavitation and bubbler system noise. Based on this data, software models for masking in beluga whales were developed. More than 20 years later, this dataset remains the only one on masking in cetaceans using both complex signals (actual vocalizations) and complex noise (actual recordings of Arctic ambient and anthropogenic noise)—highlighting both Dave Farmer’s foresight as well as perhaps his lesser-known escapades into marine mammal bioacoustics.
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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 | 0.000 | 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 | 0.000 |
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".