Vocal behavior of black‐fronted titi monkeys (<i>Callicebus nigrifrons</i>): Acoustic properties and behavioral contexts of loud calls
Bibliographic record
Abstract
Loud calls can be heard over long distances due to their high amplitude and low frequency. These calls are commonly used for both within- and between-group communication in many bird and primate species. In the Neotropics, mated pairs of socially monogamous titi monkeys (genus Callicebus) emit conspicuous, coordinated loud calls. These vocalizations appear to play a role in territorial defense, a hypothesis derived from studies of only three of the 31 recognized Callicebus species. Here, we describe the acoustic properties and organization of the loud calls of black-fronted titi monkeys (Callicebus nigrifrons). We compare the behavioral and ecological contexts associated with these vocalizations to investigate their role in within- and between-group communication, resource defense, and mate defense. Black-fronted titi monkeys create loud calls by combining a finite number of syllables to form more complex phrases, which are assembled to compose long sequences of loud calls. Bioacoustic features distinguish the loud calls used in different contexts, involving communication within- and between-groups. We found support for the hypothesis that vocalizations used for between-group communication are cooperative displays used by the mated pair and other group members to regulate access to important food resources, such as fruits. On the other hand, we only found weak support for the mate defense hypothesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".