Killer whale call frequency is similar across the oceans, but varies across sympatric ecotypes
Bibliographic record
Abstract
Killer whale populations may differ in genetics, morphology, ecology, and behavior. In the North Pacific, two sympatric populations ("resident" and "transient") specialize on different prey (fish and marine mammals) and retain reproductive isolation. In the eastern North Atlantic, whales from the same populations have been observed feeding on both fish and marine mammals. Fish-eating North Pacific "residents" are more genetically related to eastern North Atlantic killer whales than to sympatric mammal-eating "transients." In this paper, a comparison of frequency variables in killer whale calls recorded from four North Pacific resident, two North Pacific transient, and two eastern North Atlantic populations is reported to assess which factors drive the large-scale changes in call structure. Both low-frequency and high-frequency components of North Pacific transient killer whale calls have significantly lower frequencies than those of the North Pacific resident and North Atlantic populations. The difference in frequencies could be related to ecological specialization or to the phylogenetic history of these populations. North Pacific transient killer whales may have genetically inherited predisposition toward lower frequencies that may shape their learned repertoires.
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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 | 0.001 | 0.001 |
| 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".