Tusâven aivik and utjuk? Observing bearded seal and walrus seasonal presence and underwater sounds from year-round ocean acoustic recordings in the Torngat Area of Interest, Nunatsiavut, Canada
Bibliographic record
Abstract
Bearded seals (Erignathus barbatus) and walrus (Odobenus rosmarus) are Arctic species that rely on sea ice for essential life functions such as mating, pupping, and foraging. Inuit have extensive knowledge of these animals in the waters of Nunatsiavut, Canada, but there is a paucity of information regarding their seasonal distribution and behavior in the northernmost areas of the region, particularly off the Torngat Area of Interest (TAOI). Both species produce distinctive underwater sounds that are readily detected and identified, making them ideal for studies using passive acoustic monitoring (PAM). This study provides observations of the seasonal acoustic presence and behavior of walrus and bearded seals using underwater recordings collected 20 km east of Saglek Bay in the TAOI, from October 2022 to September 2023. Analyses of acoustic data were conducted to detect bearded seal and walrus vocalizations at a temporal resolution of one-hour. Relationships between acoustic presence and environmental factors, such as sea ice concentration and time of day are examined. Bearded seal trills associated with male mating displays were detected from November through June, increasing with sea ice formation and continuing weeks after sea ice retreat and open water. Walrus knocks were detected primarily during ice cover from mid-January to early May, with vocal activity also peaking during 100% ice-covered period. Bearded seals exhibited diel patterns in vocalization with significantly fewer vocalizations detected during daylight hours during November to March. These findings contribute to a baseline understanding of the acoustic presence and behavior of walruses and bearded seals in the TAOI and provide valuable insights for marine spatial planning in the waters of Nunatsiavut.
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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 | 0.000 |
| 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 | 0.001 | 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".