Bibliographic record
Abstract
Bioengineered simulations of dynamic events in the human masticatory system are relatively new. A primary advantage is their ability to integrate structure and function in cause-and-effect scenarios. By permitting detailed analyses of these interactions, and the prototyping of prosthetic additions, the models generate working hypotheses. Significant issues in their use include the importation and measurement of structural geometry, the choice of parameters affecting dynamics (e.g. inertial properties and viscoelasticities) and the nature of the modeling process (e.g. whether models are kinetically driven by muscle contraction, or kinematically defined by movement channels). Presently, there are few accepted standards or conventions for managing these computational data in the human jaws, and the data used are often derived from multiple and disparate sources. This review focuses on the approaches, assumptions, and key applications of dynamic modeling in the human masticatory system. It considers the role of imaging, the restrictions imposed by assumptions of unknown or unverifiable data, and how modeling can be a useful research technique despite these hurdles. The review concludes with a comment on creating virtual models for educational purposes.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".