Acoustic correlates of caller identity and affect intensity in the vowel-like grunt vocalizations of baboons
Bibliographic record
Abstract
Comparative, production-based research on animal vocalizations can allow assessments of continuity in vocal communication processes across species, including humans, and may aid in the development of general frameworks relating specific constitutional attributes of callers to acoustic-structural details of their vocal output. Analyses were undertaken on vowel-like baboon grunts to examine variation attributable to caller identity and the intensity of the affective state underlying call production. Six hundred six grunts from eight adult females were analyzed. Grunts derived from 128 bouts of calling in two behavioral contexts: concerted group movements and social interactions involving mothers and their young infants. Each context was subdivided into a high- and low-arousal condition. Thirteen acoustic features variously predicted to reflect variation in either caller identity or arousal intensity were measured for each grunt bout, including tempo-, source- and filter-related features. Grunt bouts were highly individually distinctive, differing in a variety of acoustic dimensions but with some indication that filter-related features contributed disproportionately to individual distinctiveness. In contrast, variation according to arousal condition was associated primarily with tempo- and source-related features, many matching those identified as vehicles of affect expression in other nonhuman primate species and in human speech and other nonverbal vocal signals.
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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 | 0.000 |
| 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".