Effect of ionizing radiation and chewing simulation on human enamel and zirconia
Bibliographic record
Abstract
PURPOSE: To evaluate the effect of ionizing irradiation on human enamel and zirconia after chewing simulation. METHODS: Twenty enamel and twenty translucent Yttria-stabilized zirconia (Y-PSZ) specimens were divided in 4 groups: Co (control) - no irradiation on enamel cusps/opposing zirconia slabs; E70 - irradiated (70 Gray) enamel cusps/opposing irradiated enamel slabs; Z70 - irradiated zirconia cusps/opposing irradiated zirconia slabs; EZ70 - irradiated enamel cusps/opposing irradiated zirconia slabs. Cusps were abraded against slabs in a chewing simulator (CS - one million cycles, 80 N, artificial saliva, 37˚C). Wear and roughness of zirconia and enamel were analyzed using a stylus profilometer. The abraded enamel was analyzed by Electron probe micro-analyzer (EPMA) and zirconia was characterized by nanoindentation and X-ray diffraction. One-way analysis of variance (ANOVA) and Tukey test were used for analysis of wear, Repeated Measures and Bonferroni test for roughness, and hardness and modulus values were compared using Wilcoxan Mann Whitney rank sum test (overall 5% significance). RESULTS: Significantly higher volume loss was presented by cusps in the E70 group (p<0.001). Wear was similar between Co and EZ70 groups. There was no significant effect of irradiation on roughness of enamel or zirconia slabs (p=0.072). Irradiated Y-PSZ slabs had significantly higher hardness and modulus than non-irradiated ones and a 7% increase in m phase content was detected after irradiation. CONCLUSIONS: The opposing surface characteristics played a more significant role on enamel wear than did ionizing radiation. However, radiation affects Y-PSZ crystalline composition, hardness and modulus of elasticity.
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 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".