Whole‐field macro‐ and micro‐deformation characteristic of unbound water‐loss in dentin hard tissue
Bibliographic record
Abstract
High-resolution deformation measurements in a functionally graded hard tissue such as human dentin are essential to understand the unbound water-loss mediated changes and their role in its mechanical integrity. Yet a whole-field, 3-dimensional (3D) measurement and characterization of fully hydrated dentin in both macro- and micro-scales remain to be a challenge. This study was conducted in 2 stages. In stage-1, a stereo-digital image correlation approach was utilized to determine the water-loss and load-induced 3D deformations of teeth in a sagittal section over consecutively acquired frames, from a fully hydrated state to nonhydrated conditions for a period up to 2 hours. The macroscale analysis revealed concentrated residual deformations at the dentin-enamel-junction and the apical regions of root in the direction perpendicular to the dentinal tubules. Significant difference in the localized deformation characteristics was observed between the inner and outer aspects of the root dentin. During quasi-static loadings, further increase in the residual deformation was observed in the dentin. In stage-2, dentin microstructural variations induced by dynamic water-loss were assessed with environmental scanning electron microscopy and atomic force microscopy (AFM), showing that the dynamic water-loss induced distention of dentinal tubules with concave tubular edges, and concurrent contraction of intertubular dentin with convex profile. The findings from the current macro- and micro-scale analysis provided insight on the free-water-loss induced regional deformations and ultrastructural changes in human dentin.
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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".