Killer whale discrete pulsed call variation.
Bibliographic record
Abstract
Animals can increase and diversify vocal complexity and call content by altering internal features within a call. The Northern Resident orca pods, residing off British Columbia, each have their own dialect of structurally discrete and highly stable pulsed calls. The objective of this study is to determine if there are distinctive internal acoustic features within the defined envelope of a single discrete pulsed call (N04) which could potentially relay the signaler’s behavioral circumstance. Orca discrete pulsed calls are highly complex with varying time-frequency slopes and multiple sidebands. This analysis began with the parsing of the N04 call into different subtypes based on distinctive changes in time-frequency slopes found in the call spectrograms. Call subtypes were verified using discriminant analysis, and changes in slope trends (ascending, descending, or constant frequency) at designated locations along the calls were compared. Variations in slopes were found between subtypes predominantly in the calls’ front and terminal regions. Clear and reliable acoustic cues within a discrete pulsed call could not only provide receivers with the physical location and group affiliation of the signaler but also would alert receivers to the signaler’s behavioral state or prey catch which would be vital information for a prey-sharing species.
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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 | 0.000 | 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) | 0.002 | 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".