Divergence of a stereotyped call in northern resident killer whales
Bibliographic record
Abstract
Northern resident killer whale pods (Orcinus orca) have distinctive stereotyped pulsed call repertoires that can be used to distinguish groups acoustically. Repertoires are generally stable, with the same call types comprising the repertoire of a given pod over a period of years to decades. Previous studies have shown that some discrete pulsed calls can be subdivided into variants or subtypes. This study suggests that new stereotyped calls may result from the gradual modification of existing call types through subtypes. Vocalizations of individuals and small groups of killer whales were collected using a bottom-mounted hydrophone array in Johnstone Strait, British Columbia in 2006 and 2007. Discriminant analysis of slope variations of a predominant call type, N4, revealed the presence of four distinct call subtypes. Similar to previous studies, there was a divergence of the N4 call between members of different matrilines of the same pod. However, this study reveals that individual killer whales produced multiple subtypes of the N4 call, indicating that divergence in the N4 call is not the result of individual differences, but rather may indicate the gradual evolution of a new stereotyped call.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".