Narwhal acoustic presence in Eclipse Sound, Nunavut: relationships with sea ice and responses to ships
Bibliographic record
Abstract
The Arctic Ocean is undergoing rapid sea ice loss and increasing ship traffic, introducing potential stressors for wildlife and challenges for management and conservation. This study examines narwhal (Monodon monoceros) responses to vessels in eastern Eclipse Sound, Nunavut, Canada using underwater acoustic recordings and ship tracking data collected between 2016 and 2021. The effect of ship proximity on detection of narwhal echolocation clicks was analyzed, accounting for environmental and temporal factors affecting detection probability. Narwhal acoustic presence exhibits seasonality, peaking in July and October, and is correlated with low solar angle in both seasons and sea ice concentration during ice formation in October. Our analysis revealed an inverse relationship between ship proximity and narwhal acoustic presence in July and October, most pronounced when ships were within 20 km of the recorder in October. These distances suggest that narwhals react to broadband sound pressure levels well below 120 dB re: 1 µPa and are more sensitive to low-frequency sounds (< 1 kHz) than previously assumed. This study offers region- and population-specific insights into narwhal responses to ships, highlighting the importance of integrating long-term monitoring of wildlife, environmental conditions, and human activities to improve prediction of Arctic marine species' movements and behavior.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".