Biphonation May Function to Enhance Individual Recognition in the Dhole, <i>Cuon alpinus</i>
Bibliographic record
Abstract
Abstract Biphonation (two independent fundamental frequencies in a call spectrum) represents one of the most widespread nonlinear phenomena in mammalian vocalizations. Recently, the structure of biphonations was described in detail; however, their functions are poorly understood. For the dhole (Cuon alpinus), biphonic calls represent a prominent feature of vocal activity. In this species, the biphonic call is composed of two frequency components – the high‐frequency squeak and the low‐frequency yap, which also occur alone as separate calls. In this study, we test the hypothesis that the complication of call structure, resulting from the joining of these calls into the biphonic yap–squeak may enhance the potential for individual recognition in the dhole. We randomly selected for analysis 30 high‐frequency squeaks, 30 low‐frequency yaps and 30 biphonic yap–squeaks per animal from five subadult captive dholes (450 calls in total). Discriminant analysis, based on 10 squeak parameter values, showed 80.7% correct assignment to a predicted individual. For 10 yap parameters, the correct assignment was only 44.7%. However, the analysis based on 10 parameters of the biphonic yap–squeak, selected as best contributing to discrimination, showed 96.7% correct assignment to a predicted individual. The results provide strong support for the hypothesis tested showing that the joining of two independent calls into a common vocalization may function to enhance individual recognition in the dhole.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".