Bibliographic record
Abstract
Oesophageal, gastric, mouth, transdiaphragmatic, transpulmonary pressures, diaphragmatic EMG, sound and chest wall excursion were measured directly in 3 professional flautists whilst playing their instruments to determine: - what respiratory muscles and percent vital capacity were being used; - how mouth pressure, embouchure resistance, embouchure aperture, airflow and velocity affect sound loudness and frequency. Lung volume was estimated from transpulmonary pressure during playing and the static deflation pressure-volume curve was measured separately; flow was calculated from delta volume/delta time; embouchure resistance was calculated from mouth pressure/flow; velocity was calculated using Bernouilli's equation and mouth pressure. Staccati and sustained tones at different frequency and intensity were performed. Sound loudness was mainly related to airflow whilst sound frequency was determined by velocity. Flow and velocity were independently controlled by mouth pressure and embouchure aperture. Mean mouth pressures varied little from individual to an other (6-11 cm H(2)O) but the flautists used between 72-83% of their vital capacity suggesting inspiratory muscle activity while playing. However, rib cage and abdominal motion were different for each subject. Although different flautists use different strategies to control mouth pressure, their individual mastery of the instrument permits control of airflow and velocity to produce the desired intensity and frequency of sound.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".