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Record W2055056723 · doi:10.4012/dmj.27.780

Influence of abrasive particle size on surface properties of flowable composites

2008· article· en· W2055056723 on OpenAlex

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 · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsSt. Thomas Hospital
FundersTokyo Medical and Dental University
KeywordsMaterials scienceComposite materialAbrasiveParticle sizeParticle (ecology)Chemical engineering

Abstract

fetched live from OpenAlex

The purpose of this investigation was to measure and compare both the surface roughness and gloss of flowable composites polished with standardized silicone carbide (SiC) papers. Four flowable and two conventional composites were used in this study. Polymerized specimens were subjected to a polishing procedure comprising 12 sequential steps from coarser to finer grits of SiC paper. At the initial polishing stage, flowable composites were more sensitive to the size of the polishing particles and thus yielded surfaces rougher than the conventional composites. Surface roughness became stable when polishing particles less than 13 microm size were used. However, although surface roughness was reduced, an esthetic gloss quality was not achieved on the resultant polished surface. On the influence of filler shape, composites with spherical fillers seemed to have the upper-hand advantage of attaining a high gloss by polishing. On the influence of polishing particle size, it was suggested that polishing should be completed with polishing particles less than 12 microm size so as to achieve clinically satisfactory surface roughness and gloss.

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 score0.166
Threshold uncertainty score0.414

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.194
Teacher spread0.184 · 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