Determining killer whale (<i>Orcinus orca</i>) call variability from passive acoustic monitoring in the Chukchi and Bering Sea, Alaska
Bibliographic record
Abstract
Killer whales (Orcinus orca) are a highly vocal species that produce three types of vocalizations; pulsed calls, whistles, and clicks. Unlike the Northern and Southern Resident populations of western Canada and the Pacific Northwest, little is known regarding the acoustic behavior of resident and transient killer whale populations north of the Aleutian Islands in the Bering and Chukchi Seas. Acoustic data were analyzed from moored recorders deployed by the Marine Mammal Laboratory at two sites each in the Bering and Chukchi Seas (BOEM-funded). The recorders sampled at 4 kHz on a 7% duty cycle (Bering) or 16 kHz on a 28% duty cycle (Chukchi). Over 1100 calls were identified, and discrete call classification was conducted using an alphanumerical system that distinguished calls by location, general contour, and segment variation. Parameters analyzed included call duration, start/end frequency, and delta frequency/time; periods of call repetition were common. These results will help determine if resident populations occur in the Chukchi Sea, and identify which transient populations are present. This initial work classifying killer whale sounds in the Arctic and along the Bering Sea shelf will also facilitate comparisons of call types within and among transient and resident killer whale populations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".