Stress Analysis of the Dynamic Process Simulation of Canine Tipping Movement during a Therapy Period
Bibliographic record
Abstract
With combining bone remodeling with finite element analysis,the dynamic process of canine tipping movement during a therapy period is simulated and the stress distribution in each phase is obtained.A three-dimensional finite element model including tooth,periodontal ligament,pulp and alveolar bone is established according to a mandibular canine of an orthodontic patient.Through finite element analysis,the strain distribution of the model is obtained under the initial moment.The normal strain on the surface of the periodontal ligament is assumed as the mechanical stimulus proportional to the absorption rate of alveolar bone in each week.Then the second model based on the moved canine is constructed.With the decreased moment,the computed normal strain of the periodontal ligament surface is also used for the next phase of tooth tipping movement.In a therapy period,the relationship between the time and the degree in the numerical simulation is consistent with typical tooth movement.The stresse in the alveolar bone and periodontal ligament gradually decreases and the maximum stress appears at the tooth apex or the crest of alveolar bone.The dynamic process simulation of canine tipping movement is achieved.It will be helpful for the planning and forecast of the therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".