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Record W2095218233 · doi:10.1121/1.3183592

Ranking vocal fold model parameters by their influence on modal frequencies

2009· article· en· W2095218233 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2009
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsMcGill University
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsNational Institute on Deafness and Other Communication DisordersNational Science Foundation
KeywordsVocal foldsFold (higher-order function)ModalAcousticsPhonationComputer scienceSet (abstract data type)Finite element methodMathematicsStructural engineeringPhysicsEngineeringLarynxMaterials science

Abstract

fetched live from OpenAlex

The purpose of this study was to identify, using computational models, the vocal fold parameters which are most influential in determining the vibratory characteristics of the vocal folds. The sensitivities of vocal folds modal frequencies to variations model parameters were used to determine the most influential parameters. A detailed finite element model of the human vocal fold was created. The model was defined by eight geometric and six material parameters. The model included transitional boundary regions to idealize the complex physiological structure of real human subjects. Parameters were simultaneously varied over ranges representative of actual human vocal folds. Three separate statistical analysis techniques were used to identify the most and least sensitive model parameters with respect to modal frequency. The results from all three methods consistently suggest that a set of five parameters are most influential in determining the vibratory characteristics of the vocal folds.

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.

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 score0.523
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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