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Record W3184254530 · doi:10.1121/10.0005596

Vocal fold dynamics in a synthetic self-oscillating model: Contact pressure and dissipated-energy dose

2021· article· en· W3184254530 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
KeywordsDissipationMechanicsOscillation (cell signaling)Dynamics (music)Contact dynamicsCollisionResolution (logic)PhysicsMaterials scienceChemistryAcousticsThermodynamicsComputer science

Abstract

fetched live from OpenAlex

The energy dissipated during vocal fold (VF) contact is a predictor of phonotrauma. Difficulty measuring contact pressure has forced prior energy dissipation estimates to rely upon generalized approximations of the contact dynamics. To address this shortcoming, contact pressure was measured in a self-oscillating synthetic VF model with high spatiotemporal resolution using a hemilaryngeal configuration. The approach yields a temporal resolution of less than 0.26 ms and a spatial resolution of 0.254 mm in the inferior-superior direction. The average contact pressure was found to be 32% of the peak contact pressure, 60% higher than the ratio estimated in prior studies. It was found that 52% of the total power was dissipated due to collision. The power dissipated during contact was an order of magnitude higher than the power dissipated due to internal friction during the non-contact phase of oscillation. Both the contact pressure magnitude and dissipated power were found to be maximums at the mid anterior-posterior position, supporting the idea that collision is responsible for the formation of benign lesions, which normally appear at the middle third of the VF.

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 score0.561
Threshold uncertainty score0.267

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 integrity0.0000.000
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 head0.009
GPT teacher head0.249
Teacher spread0.241 · 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

Citations20
Published2021
Admission routes1
Has abstractyes

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