An acoustic model of the respiratory tract
Bibliographic record
Abstract
With the emerging use of tracheal sound analysis to detect and monitor respiratory tract changes such as those found in asthma and obstructive sleep apnea, there is a need to link the attributes of these easily measured sounds first to the underlying anatomy, and then to specific pathophysiology. To begin this process, we have developed a model of the acoustic properties of the entire respiratory tract (supraglottal plus subglottal airways) over the frequency range of tracheal sound measurements, 100 to 3000 Hz. The respiratory tract is represented by a transmission line acoustical analogy with varying cross sectional area, yielding walls, and dichotomous branching in the subglottal component. The model predicts the location in frequency of the natural acoustic resonances of components or the entire tract. Individually, the supra and subglottal portions of the model predict well the distinct locations of the spectral peaks (formants) from speech sounds such as /a/ as measured at the mouth and the trachea, respectively, in healthy subjects. When combining the supraglottic and subglottic portions to form a complete tract model, the predicted peak locations compare favorably with those of tracheal sounds measured during normal breathing. This modeling effort provides the first insights into the complex relationships between the spectral peaks of tracheal sounds and the underlying anatomy of the respiratory tract.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".