Spatiotemporal Patterns and Habitat Preferences of Bowhead Whales in the Eastern Beaufort Sea, Arctic Ocean
Bibliographic record
Abstract
Τhe Arctic is warming four times faster than the rest of the globe.The shrinking sea ice causes cascading effects throughout the ecosystem.While cetaceans experience climate-driven changes in the ocean, their adaptation mechanisms include spatially and/or temporally shifting their habitat occupancy, or even permanently altering their migration phenology.The urgent need for monitoring Arctic cetaceans, combined with the challenge of long-term studies in the Arctic, was addressed with passive acoustics.During 2014-2021, ten sites in the Beaufort Sea were equipped with fixed acoustic recorders, monitoring the ocean soundscape for 1-12 months.Combined manual and automated bioacoustic analysis with statistical analysis allowed quantifying the variability of bowhead whale (Balaena mysticetus) presence through time and space.The bowhead is the only Arctic endemic mysticete and a species of high cultural and nutritional value for the Inuit people.Results indicate a large variation in bowhead presence over the years and across the stations.However, a clear seasonal pattern is dominant throughout the data.These spatiotemporal patterns, combined with in-situ and remotely-sensed environmental variables in multivariate models allowed identifying the conditions that affect the bowhead distribution.Understanding these responses is key ---------
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".