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Record W1973123457 · doi:10.1121/1.4806674

Modeling incomplete glottal closure due to a posterior glottal opening and its effects on the dynamics of the vocal folds

2013· article· en· W1973123457 on OpenAlexaff
Matías Zañartu, Byron D. Erath, Sean D. Peterson, Robert E. Hillman, George R. Wodicka

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVocal foldsAcousticsPhonationVocal tractMathematicsMechanicsPhysicsAnatomyMedicineAudiology

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

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