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Forces resisting jaw displacement in relaxed humans: a predominantly viscous phenomenon

2002· article· en· W1977253405 on OpenAlexaff
Christopher C. Peck, Anmol Sooch, A.G. Hannam

Bibliographic record

VenueJournal of Oral Rehabilitation · 2002
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersMedical Research Council
KeywordsViscoelasticityMasticatory forceMechanicsDisplacement (psychology)Elasticity (physics)Bite force quotientBiomechanicsPhysicsAnatomyMaterials scienceMathematicsOrthodonticsBiologyMedicineThermodynamicsPsychology

Abstract

fetched live from OpenAlex

Forces opening the relaxed human jaw are resisted by intrinsic restraints, including passive tensions in the jaw-closing muscles. These muscle tensions have been modelled as viscoelastic elements, and static measurements suggest their elastic portions contribute approximately a total of 5 N resistance at wide gape. As the viscous damping properties of muscles which affect the jaw's dynamic behaviour are unknown, we measured the jaw opening force required to reach maximum gape during fast and slow opening in six relaxed subjects. These data were then incorporated in a dynamic mathematical jaw model to determine the damping properties of the masticatory system. During the 3 and 8 s opening trials, forces increased with gape (6.7 +/- 3.3 and 3.9 +/- 2.3 N, respectively, at 50% gape) and reached their maxima at wide gape (19.9 +/- 4.5 and 13.2 +/- 4.4 N, respectively). The muscle damping constant needed by the model to emulate these results was 150 Nsm(-1), approximately 25% lower than the calculated critical damping constant. This study suggests low forces are required to open the jaw in relaxed humans, and that jaw viscosity, not elasticity, provides the major resistance to motion.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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 designObservational
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

Citations34
Published2002
Admission routes1
Has abstractyes

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