Characteristics of nonlinear phenomena in the tonal vocalizations of a North American canid, <i>Canis rufus</i>
Bibliographic record
Abstract
This study examines the structure and frequency of occurrence of nonlinear phenomena found in tonal vocalizations produced by red wolves. Spectrograms were obtained from audio tracks of digital video recordings of captive wolves from a breeding facility in Graham, WA. Tonal vocalizations were determined to be composed of 1–30 sound units, arranged in 1–5 phrases. Linear units included squeaks (2600–9600 Hz) and wuhs (100–1600 Hz); nonlinear units accounted for 22% of sounds and included between-type frequency jumps, harmonic and pure-tone biphonations, squeaks with sidebands, and squeak jumps. Five tonal vocalization types were identified based on unit composition: squeaks (48.4%), wuhs (19.3%), and three mixed vocalizations: banded squeaks (13.2%), complex squeaks (6.4%), and squeak-wuhs (12.2%). Unit order within a squeak-wuh vocalization was not random; transitions between units following a structural gradient most likely begin with higher frequency units and end with mixed or lower frequency units. The production of nonlinear sounds varied within and between individuals. The linear and nonlinear structure of red wolf tonal vocalizations are similar to that which has been reported in dholes and African wild dogs, and which have been indicated in reviews of published sonograms of gray wolf vocalizations.
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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) | 0.001 | 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".