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Record W1984164080 · doi:10.1115/sbc2013-14034

Incorporation of Stress-Dependency in the Modeling of Midpalatal Suture Behavior During Maxillary Expansion Treatment

2013· article· en· W1984164080 on OpenAlexaff
Dan L. Romanyk, S. S. Liu, Michael Lipsett, Manuel O. Lagravère, Roger Toogood, Paul W. Major, Jason P. Carey

Bibliographic record

VenueVolume 1B: Extremity; Fluid Mechanics; Gait; Growth, Remodeling, and Repair; Heart Valves; Injury Biomechanics; Mechanotransduction and Sub-Cellular Biophysics; MultiScale Biotransport; Muscle, Tendon and Ligament; Musculoskeletal Devices; Multiscale Mechanics; Thermal Medicine; Ocular Biomechanics; Pediatric Hemodynamics; Pericellular Phenomena; Tissue Mechanics; Biotransport Design and Devices; Spine; Stent Device Hemodynamics; Vascular Solid Mechanics; Student Paper and Design Competitions · 2013
Typearticle
Languageen
FieldMedicine
TopicFacial Trauma and Fracture Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaxillaOrthodonticsStress (linguistics)Computer scienceFibrous jointDentistryMedicine

Abstract

fetched live from OpenAlex

In order to widen a patient’s upper jaw, or maxilla, orthodontists will make use of a maxillary expansion (ME) appliance. This procedure is typically used in adolescents having a narrow maxillary complex. Widening the jaw may be achieved through a variety of means such as expansion screws or springs [1]; however, they are all utilized to generate additional space allowing for correction of various tooth misalignments. An example of a common appliance is provided in Fig. 1a.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.002
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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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