I know what you did last winter: Bowhead whale anomalous winter acoustic occurrence patterns in the Beaufort Sea, 2018–2020
Bibliographic record
Abstract
Global warming is affecting the Arctic at a faster pace than the rest of the world, causing an urgent need to monitor ecosystems to detect possible climate-related changes. To this end, five passive acoustic datasets were recorded in the southern Amundsen Gulf (eastern Beaufort Sea) from September 2018 to September 2020 and analyzed for Bering-Chukchi-Beaufort (BCB) bowhead whale calls using a combination of automated and manual detections. Results indicate a large variation in bowhead occurrence patterns between the two years. For 2018–2019, we obtained the first evidence of bowheads overwintering in what is typically their summer foraging ground. Examination of the following year’s recordings sheds light on whether this interruption in bowhead annual migration was an anomaly or part of an ongoing phenological shift due to climate change. Time series of remotely sensed sea ice concentration at the study area were considered over the last seven years in interpreting differences in migratory behavior of the whales. Statistical quantification of seasonal patterns and habitat preferences of bowheads, based on the 2018–2019 acoustic data, are presented to provide context to BCB bowhead ecology. Passive acoustic monitoring is an indispensable tool in discerning whale responses to a changing ocean in the Arctic.
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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 | 0.001 |
| 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".