Vocal repertoire of South American fur seals, <i>Arctocephalus australis</i>: structure, function, and context
Bibliographic record
Abstract
We describe the vocal repertoire of male and female South American fur seals (Arctocephalus australis) breeding at Punta San Juan, Peru, the first such description for any member of the genus. We distinguished 11 call types, which we grouped into four functional classes: investigative, threat, submissive, and affiliative calls. Barking is used during non-agonistic investigation of other individuals. Threat calls of South American fur seals show gradation of structure, form, and apparent meaning, and are grouped into two series: nontonal or respiratory sounds, and pulsed or guttural sounds that sometimes include a terminal tonal component. This might be adaptive in enabling display behavior to be flexible in agonistic situations, allowing participants to interpret situations on the basis of contextual cues and their own physical ability and experience. In contrast, vocal displays such as submissive, full-threat, and affiliative calls tend to have a discrete acoustic structure. Of these, full-threat, female-attraction, and pup-attraction calls share acoustic characteristics: all are long, loud calls composed of both pulsed and tonal components, and show sufficient variation to allow individual recognition. We attempt to establish a base line for standardizing nomenclature and acoustic analysis, to facilitate further comparative research on the vocal repertoire of Arctocephalus species.
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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 | 0.001 | 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) | 0.002 | 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".