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Record W2077019628 · doi:10.1080/21681163.2013.851627

Speech function of the oropharyngeal isthmus: a modelling study

2014· article· en· W2077019628 on OpenAlexaff
Bryan Gick, Peter J. Anderson, Hui Chen, Chenhao Chiu, Ho‐Beom Kwon, Ian Stavness, Ling Tsou, Sidney Fels

Bibliographic record

VenueComputer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · 2014
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity of SaskatchewanUniversity of British Columbia
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsTongueSoft palateFinite element methodVocal tractFlexibility (engineering)Speech productionComputer scienceNoise (video)AnatomyMaxillaSpeech recognitionMedicineMathematicsEngineeringStructural engineeringArtificial intelligencePathologySurgery

Abstract

fetched live from OpenAlex

A finite element method (FEM) based numerical model of upper airway structures (jaw, tongue, maxilla, soft palate) was implemented to observe interactions between the soft palate and tongue, and in particular to distinguish the contributions of individual muscles in producing speech-relevant constrictions of the oropharyngeal isthmus (OPI), or "uvular" region of the oral tract. Simulations revealed a sphincter-like general operation for the OPI, particularly with regard to the function of the palatoglossus muscle. Further, as has been observed with the lips, the OPI can be controlled by multiple distinct muscular mechanisms, each reliably producing a different sized opening and robust to activation noise, suggestive of a modular view of speech motor control. As off-midline structures of the OPI are difficult to observe during speech production, biomechanical simulation offers a promising approach to studying these structures.

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)0.001
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.322
Teacher spread0.305 · 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

Citations24
Published2014
Admission routes1
Has abstractyes

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