The effect of five kinds of surface treatment agents on the bond strength to various ceramics with thermocycle aging
Bibliographic record
Abstract
This study evaluated the effects of ceramic surface treatment agents on shear bond strengths to ceramic materials with and without thermocycling. Ceramic plates were prepared from feldspathic ceramic; AAA, lithium disilicate ceramic material; IPS e.max Press, zirconia ceramic; Lava. Ceramic surfaces were pretreated with one of five surface treatment agents (Clearfil PhotoBond mixed with Porcelainbond activator (PB), Clearfil SE One mixed with Porcelainbond activator (SO), Ceramic Primer (CP), Universal Primer (UP), Scotchbond Universal (SU)), and then a resin cement (Clapearl DC) was filled. After 0, 5,000, and 10,000 thermocycles, micro-shear bond strengths between ceramic-cement interfaces were determined. SU exhibited significantly lower initial bond strength to AAA and e.max than PB, SO, CP, and UP. For Lava, PB, SO, CP and SU exhibited higher initial bond strengths than UP. Thermocycles reduced bond strengths to all the ceramic materials with any surface treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".