Measurement of biomechanical behavior of dentin hard tissue in response to unbound water loss using stereo-digital image correlation
Bibliographic record
Abstract
Abstract Understanding the biomechanical behavior of dentin hard tissue with fluid-filled dentin tubules and hydrated matrices is essential for studying this functionally graded biological composite. The stereo-digital image correlation technique with an adaptive high-magnification field of view (FOV) for fully hydrated biological tissue measurement was investigated. The adaptive magnification is controlled by the length of extension tubes. To determine both the unbound water loss induced and load-induced three-dimensional (3D) deformation of dentin hard tissue from a fully hydrated state to a non-hydrated condition, samples of dentin blocks and half teeth in sagittal sections were studied for a period of 2 h in situ over varied speckle patterns. The effects of speckles on water evaporation, camera pre-heating, and measurement accuracy in the wet, curved and long-term measurement were analyzed. The elastic modulus and Poisson’s ratio of both dentin and pulp in response to unbound water evaporation were measured. With the unbound water loss, the mean values of the elastic modulus generally increased from ∼8 GPa to ∼10 GPa in pulp region and from ∼10 GPa to ∼12 GPa in dentin region. The mean values of the Poisson’s ratio increased both in pulp and in dentin. Poisson’s ratio in the dentin regions (∼0.3) were generally smaller than those in the pulp regions (even can reach 0.6), irrespective of the partial dehydration time. Further analysis of the full-field deformation results provided insight into the unbound water-induced regional deformations and mechanical changes in human dentin. It’s found that the unbound water loss induced deformations were more prominent when compared to load induced deformations.
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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 | 0.000 |
| 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) | 0.001 | 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".