Asymmetries in the individual distinctiveness and maternal recognition of infant contact calls and distress screams in baboons
Bibliographic record
Abstract
A key component of nonhuman primate vocal communication is the production and recognition of clear cues to social identity that function in the management of these species' individualistic social relationships. However, it remains unclear how ubiquitous such identity cues are across call types and age-sex classes and what the underlying vocal production mechanisms responsible might be. This study focused on two structurally distinct call types produced by infant baboons in contexts that place a similar functional premium on communicating clear cues to caller identity: (1) contact calls produced when physically separated from, and attempting to relocate, mothers and (2) distress screams produced when aggressively attacked by other group members. Acoustic analyses and field experiments were conducted to examine individual differentiation in single vocalizations of each type and to test mothers' ability to recognize infant calls. Both call types showed statistically significant individual differentiation, but the magnitude of the differentiation was substantially higher in contact calls. Mothers readily discriminated own-offspring contact calls from those of familiar but unrelated infants, but did not do so when it came to distress screams. Several possible explanations for these asymmetries in call differentiation and recognition are considered.
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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".