Quantifying northern bottlenose and sperm whale acoustic behavioural responses to anthropogenic noise in Baffin Bay, Canada
Bibliographic record
Abstract
Marine mammals rely on their auditory system for a myriad of life functions (e.g., navigating, foraging, socializing) and consequently, are vulnerable to loud human activities (e.g., vessel traffic, fishing, military activities). These activities can impede communication, cause behavioural disturbances, and can even cause injuries. As the Arctic warms and sea ice coverage decreases, more opportunities for human activities are arising. How noise impacts the acoustic behaviour of Arctic marine mammals is unclear. In October 2022 and 2023 controlled noise exposure experiments were conducted using military sonar (source level of 176.4 dB re 1 μPa) on northern bottlenose and sperm whales in Baffin Bay while they were foraging around vessels actively fishing. Hydrophone suction-cup biologgers (DTAGs; n = 5, ∼72 cumulative hours) were used to capture vessel and sonar noises, and whale vocalizations before, during, and after the noise exposure periods. Using a click detector, focal whale clicks were identified and quantitatively compared to received noise levels. This information will then be used to determine noise thresholds for acoustical behavioural responses. These results will support risk-mitigation strategies for the Department of National Defence Canada and Fisheries and Oceans Canada, as well as address Inuit concerns about the effects of military sonar on marine mammals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".