Nonlinear phenomena in animal vocalizations: do they reflect alternative functional modes of voice control, ‘leaked’ cues to quality or condition, or both?
Bibliographic record
Abstract
Nonlinear phenomena (NLP) in animal vocalizations typically present as abrupt departures from normative controlled voicing. They occur most commonly in loud vocalizations, often in contexts of high arousal, including alarm, aggression, fear or distress, or in elaborate displays of territory or competitive ability. They therefore invite interpretation as ‘mistakes’ that evince loss of vocal control resulting from effortful, emotional ‘over-driving’ of the vocal system. However, vocal over-driving may be more flexible and purposeful, representing an alternative functional mode of voice control if NLP can benefit signallers in some contexts. The latter perspective is first elaborated with examples from non-human primates before turning to cases where NLP truly do evince loss of vocal control that may then ‘leak’ cues to signaller quality or condition. To support future frameworks to study and understand the different domains where NLP occur, a functional distinction is emphasized that turns on whether high-amplitude, effortful voicing—which inherently predisposes NLP—is at the discretion of the signaller such that the focus is on the adaptive production of NLP, or whether effortful voicing is effectively forced upon signallers by other dictates of the context itself, changing the focus to being the adaptive avoidance of NLP. This article is part of the theme issue ‘Nonlinear phenomena in vertebrate vocalizations: mechanisms and communicative functions’.
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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 | 0.001 | 0.002 |
| 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 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".