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Record W2734587921 · doi:10.4012/dmj.2016-383

The effect of five kinds of surface treatment agents on the bond strength to various ceramics with thermocycle aging

2017· article· en· W2734587921 on OpenAlex
Yukari Noda, Masatoshi Nakajima, Masahiro Takahashi, Teerapong Mamanee, Keiichi Hosaka, Tomohiro Takagaki, Masaomi Ikeda, Richard M. FOXTON, Junji Tagami

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDental Materials Journal · 2017
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMaterials scienceCeramicBond strengthComposite materialCubic zirconiaLithium disilicatePrimer (cosmetics)AdhesiveLayer (electronics)

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.285
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it