Vocalization-associated respiration patterns: thermography-based monitoring and detection of preparation for calling
Bibliographic record
Abstract
Vocal emission requires coordination with the respiratory system. Monitoring the increase in laryngeal pressure, which is needed for vocal production, allows detection of transitions from quiet respiration to vocalization-supporting respiration. Characterization of these transitions could be used to identify preparation for vocal emission and to examine the probability of it manifesting into an actual vocal production event. Specifically, overlaying the subject's respiration with conspecific calls can highlight events of call initiation and suppression, as a means of signalling coordination and avoiding jamming. Here, we present a thermal imaging-based methodology for synchronized respiration and vocalization monitoring of free-ranging meerkats. The sensitivity of this methodology is sufficient for detecting transient changes in the subject's respiration associated with the exertion of vocal production. The differences in respiration are apparent not only during the vocal output, but also prior to it, marking the potential time frame of the respiratory preparation for calling. A correlation between conspecific calls with elongation of the focal subject's respiration cycles could be related to fluctuations in attention levels or in the motivation to reply. This framework can be used for examining the capability for enhanced respiration control in animals during modulated and complex vocal sequences, detecting 'failed' vocalization attempts and investigating the role of respiration cues in the regulation of vocal interactions.
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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 | 0.001 |
| 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 | 0.001 | 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".