Modeling incomplete glottal closure due to a posterior glottal opening and its effects on the dynamics of the vocal folds
Bibliographic record
Abstract
Even though incomplete glottal closure is present in normal and pathological voices, it has received little attention in self-sustained models of phonation. The effects of acoustic interaction due to a posterior glottal gap on the tissue dynamics, energy transfer, and glottal aerodynamics were numerically investigated. The domain was prescribed as flow through two separate orifices (posterior gap and membranous vocal folds) that merge in the supraglottal tract, with the governing flow equations determined from a control volume analysis based on conservation of mass and linear momentum. The equations of motion remained unaffected, although the driving forces were indirectly altered through the acoustic interaction. The method was implemented using the body-cover model, wave-reflection-analog sound propagation, and a boundary-layer asymmetric flow solver. The inclusion of a gap area of 0.03 cm2 reduced the RMS energy transfer from the fluid to the vocal folds by 20 % and radiated SPL by 5 dB. When compensating for this reduction with an increased subglottal pressure to match the same SPL, a significant increase in MFDR and AC flow was noted, thus mimicking vocal hyperfunction. In addition, larger gap areas yielded less glottal pulse skewing and a glottal airflow proportional to the transglottal pressure drop.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".