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Record W3122713933 · doi:10.22215/etd/2016-11308

Numerical Investigations of Pathological Phonation Resulting From a Unilateral Sessile Polyp

2016· dissertation· en· W3122713933 on OpenAlexaff
Raymond Greiss

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsCarleton University
Fundersnot available
KeywordsGlottisPhonationVocal foldsVibrationStiffnessAdded massMechanicsFinite element methodBernoulli's principleAcousticsPhysicsStructural engineeringLarynxMaterials scienceAnatomyMedicineEngineeringAudiology

Abstract

fetched live from OpenAlex

The voice, generated via the modulation of airflow by the vocal folds in the larynx, is studied computationally to improve treatment of voice disorders.A model of a vocal fold with a polyp is developed, and implemented in-house.Structural vibration of the model is computed in the time and frequency domain with the finite element method.Aerodynamic loading is computed with the Bernoulli equation, which is corrected for viscous and unsteady effects.A polyp's mass is the most influential parameter, followed by stiffness and position.Natural frequencies are decreased by increasing mass, and increased by increasing stiffness.Damping due to the polyp's mass is mitigated near fixed walls.Polyp mass distribution is most influential on vibration when spread into the glottis.Pitch-intensity dependence decreases with increasing polyp mass.The polyp disrupts vibration of the vocal fold pair by decreasing the cross-sectional area of the flow, and vibrating out of phase.My first steps into the world of scholarly research would not have been so rewarding if it were not for the immense support offered to me by my supervisors, Professor Joana Rocha and Professor Edgar Matida.Without their dependable technical and financial support, this work would not have been possible.Most importantly, they fostered an open environment where there is no fear of asking a stupid question.To you both, I am exceedingly grateful.Despite my fondness for numerical computing, I've learned the phrase, "Too much of anything can kill you," to ring true.My thanks go out to Colin, Zahra, Sarah, and Jared for the numerous distracting, yet stimulating, coffee breaks which they have accompanied me on.Their willingness to allow me to bounce ideas off of them have led to the refinement of many aspects of this thesis.I would not be in a position to pursue graduate studies without the backing of my family, who have always enthusiastically encouraged me to pursue my interests, whether academic or not.Thank you for your persistent love, care, and confidence.I would not have been able to overcome my confusion over turbulence, nor inner turbulence, without your inspiration.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.304
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2016
Admission routes1
Has abstractyes

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