Bibliographic record
Abstract
PURPOSE: The original ceramic surface finish and its microstructure may have an effect on crack propagation. The purpose of this study was to investigate the relation between crack propagation and ceramic microstructure following cyclic fatigue loading, and to qualitatively evaluate and quantitatively measure the surface and subsurface crack depths of three types of ceramic restorations with different microstructures using a Confocal Laser Scanning Microscope (CLSM) and Scanning Electron Microscope (SEM). MATERIALS AND METHODS: Twenty (8 x 4 x 2 mm(3)) blocks of AllCeram (AC), experimental ceramic (EC, IPS e.max Press), and Sensation SL (SSL) were prepared, ten glazed and ten polished of each material. Sixty antagonist enamel specimens were made from the labial surfaces of permanent incisors. The ceramic abraders were attached to a wear machine, so that each enamel specimen presented at 45 degrees to the vertical movement of the abraders, and immersed in artificial saliva. Wear was induced for 80K cycles at 60 cycles/min with a load of 40 N and 2-mm horizontal deflection. The specimens were examined for cracks at baseline, 5K, 10K, 20K, 40K, and 80K cycles. RESULTS: Twenty- to 30-microm deep subsurface cracking appeared in SSL, with 8 to 10 microm in AC, and 7 microm close to the margin of the wear facets in glazed EC after 5K cycles. The EC showed no cracks with increasing wear cycles. Seventy-microm deep subsurface cracks were detected in SSL and 45 microm in AC after 80K cycles. Statistically, there was significant difference among the three materials (p < 0.05). Bonferroni multiple comparison of means test confirmed the ANOVA test and showed that there was no statistical difference (p > 0.05) in crack depth within the same ceramic material with different surface finishes. CONCLUSIONS: The ceramic materials with different microstructures showed different patterns of subsurface cracking.
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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) | 0.002 | 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".