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Record W2005369678 · doi:10.1002/cnm.1423

Coupled hard–soft tissue simulation with contact and constraints applied to jaw–tongue–hyoid dynamics

2010· article· en· W2005369678 on OpenAlexafffund
Ian Stavness, John E. Lloyd, Yohan Payan, Sidney Fels

Bibliographic record

VenueInternational Journal for Numerical Methods in Biomedical Engineering · 2010
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTonguePoint (geometry)Constraint (computer-aided design)Finite element methodHyoid boneComputer scienceStructural engineeringEngineeringMechanical engineeringAnatomyGeometryMathematicsMedicine

Abstract

fetched live from OpenAlex

Abstract We present an open‐source physical simulation system suitable for efficient modeling of anatomical structures composed of both hard and soft tissue components, interconnected by point‐wise attachments, contact, and other constraints. Specific attention is paid to the computational formulation needed for the coupled simulation of rigid and deformable structures, and a constraint‐based mechanism is described for attaching these together. As an application of this system, we then present a novel 3D dynamic model of the jaw–tongue–hyoid complex, consisting of an FEM model of the tongue, rigid jaw, and hyoid structures, point‐to‐point muscle actuators, and constraints for bite contact and the temporomandibular joints. Several simulations are presented showing combined jaw–tongue actions and demonstrating the effects of coupled jaw–tongue–hyoid dynamics. Copyright © 2010 John Wiley & Sons, Ltd.

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: none
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.456
Teacher spread0.430 · 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

Citations98
Published2010
Admission routes2
Has abstractyes

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Same venueInternational Journal for Numerical Methods in Biomedical EngineeringSame topicTemporomandibular Joint DisordersFrench-language works237,207