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Record W3195675338 · doi:10.1121/10.0005882

Vocal fold dynamics in a synthetic self-oscillating model: Intraglottal aerodynamic pressure and energy

2021· article· en· W3195675338 on OpenAlexaff
Mohsen Motie-Shirazi, Matías Zañartu, Sean D. Peterson, Byron D. Erath

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2021
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity of Waterloo
FundersNational Institute on Deafness and Other Communication DisordersFoundation for the National Institutes of Health
KeywordsPhonationGlottisAirflowMechanicsVocal foldsAerodynamicsOscillation (cell signaling)Closing (real estate)AcousticsFlow (mathematics)PhysicsMaterials scienceAnatomyLarynxChemistryMedicineThermodynamicsAudiology

Abstract

fetched live from OpenAlex

Self-sustained oscillations of the vocal folds (VFs) during phonation are the result of the energy exchange between the airflow and VF tissue. Understanding this mechanism requires accurate investigation of the aerodynamic pressures acting on the VF surface during oscillation. A self-oscillating silicone VF model was used in a hemilaryngeal flow facility to measure the time-varying pressure distribution along the inferior-superior thickness of the VF and at four discrete locations in the anterior-posterior direction. It was found that the intraglottal pressures during the opening and closing phases of the glottis are highly dependent on three-dimensional and unsteady flow behaviors. The measured aerodynamic pressures and estimates of the medial surface velocity were used to compute the intraglottal energy transfer from the airflow to the VFs. The energy was greatest at the anterior-posterior midline and decreased significantly toward the anterior/posterior endpoints. The findings provide insight into the dynamics of the VF oscillation and potential causes of some VF disorders.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.231
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2021
Admission routes1
Has abstractyes

Explore more

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