Long term stability and individual distinctiveness in captive orca vocalizations
Bibliographic record
Abstract
With focus on the question of signature calling in killer whales, recordings from five captive orcas (of Icelandic origin) held at Marineland of Canada were compared. For the present analysis, samples of three different call syllables were selected from recordings made five years apart and from instances in which the identity of the calling whale was unambiguous due to temporary isolation, concomitant bubbling, and/or head nodding. The Raven software package was used to ascertain the frequency range, frequency (max), duration, and timing of maximum and minimum power within each sample. For two of the three call syllables, statistically significant differences were found among the five whales for call length and for the timing of maximums and minimums (p<0.01−0.001). This similarly proved true for nearly all pairwise comparisons between whales, including mother-offspring dyads. By contrast, for three of four whales for which we had sufficient samples, no significant differences were found on any measure between samples taken from the same whales five years apart. These findings therefore support the notion that the voices of individual orcas are distinct from one another in ways that are stable over the course of multiple years.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".