Modelling the masticatory biomechanics of a pig
Bibliographic record
Abstract
The relationships between muscle tensions, jaw motions, bite and joint forces, and craniofacial morphology are not fully understood. Three-dimensional (3-D) computer models are able to combine anatomical and functional data to examine these complex relationships. In this paper we describe the construction of a 3-D dynamic model using the anatomical (skeletal and muscle form) and the functional (muscle activation patterns) features of an individual pig. It is hypothesized that the model would produce functional jaw movements similar to those recordable in vivo. Anatomical data were obtained by CT scanning (skeletal elements) and MR imaging (muscles). Functional data (muscle activities) of the same animal were obtained during chewing by bipolar intramuscular electrodes in six masticatory muscles and combined with previously published EMG data. The model was driven by the functional data to predict the jaw motions and forces within the masticatory system. The study showed that it is feasible to reconstruct the complex 3-D gross anatomy of an individual's masticatory system in vivo. Anatomical data derived from the 3-D reconstructions were in agreement with published standards. The model produced jaw motions, alternating in chewing side, typical for the pig. The amplitude of the jaw excursions and the timing of the different phases within the chewing cycle were also in agreement with previously published data. Condylar motions and forces were within expected ranges. The study indicates that key parameters of the pig's chewing cycle can be simulated by combining general biomechanical principles, individual-specific data and a dynamic modelling approach frequently used in mechanical engineering.
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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".