Dynamic mechanics in the pig mandibular symphysis
Bibliographic record
Abstract
During mastication, various biomechanical events occur at the mammalian jaw symphysis. Previously, these events have been studied in the static environment, or by direct recording of surface bone strains. Thus far, however, it has not been possible to demonstrate directly the forces and torques passing through the symphysis in association with dynamically changing muscle tensions. Therefore, we modified a previously published dynamic pig jaw model to predict the forces and torques at the symphysis, and related these to simulated masticatory muscle tensions, and bite, joint and food bolus forces. An artificial rigid joint was modelled at the symphysis, allowing measurements of the tri-axial forces and torques passing through it. The model successfully confirmed three previously postulated loading patterns at the symphysis. Dorsoventral shear occurred when the lower teeth hit the artificial food bolus. It was associated with balancing-side jaw adductor forces, and reaction forces from the working-side bite point. Medial transverse bending occurred during jaw opening, and was associated with bilateral tensions in the lateral pterygoid. Lateral transverse bending (wishboning) occurred at the late stage of the power stroke, and was associated with the actions of the deep and superficial masseters. The largest predicted force was dorsoventral shear force, and the largest torque was a 'wishboning' torque about the superoinferior axis. We suggest that dynamic modelling offers a new and powerful method for studying jaw biomechanics, especially when the parameters involved are difficult or impossible to measure in vivo.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".