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Inorganic Fillers for Dental Resin Composites: Present and Future

2015· article· en· W2322721708 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Biomaterials Science & Engineering · 2015
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health ResearchShanghai International Science and TechnologyNatural Sciences and Engineering Research Council of CanadaMitacsCalifornia HIV/AIDS Research Program
KeywordsMaterials scienceComposite materialWhiskersDental compositeBiocompatibilityFiller (materials)PolymerComposite numberWhiskerPorosity

Abstract

fetched live from OpenAlex

Dental resins represent an important family of biomaterials that have been evolving in response to the needs in biocompatibility and mechanical properties. They are composite materials consisting of mostly inorganic fillers and additives bound together with a polymer matrix. A large number of fillers in a variety of forms (spheroidal, fibrous, porous, etc.) along with other additives have been studied to enhance the performance of the composites. Silane derivatives are attached as coupling agents to the fillers to improve their interfacial properties. A review of the literature on dental composite fillers seems to suggest that each of the fillers tested presents its own strengths and weaknesses, and often combinations of these yield resin composites with the desired balance of properties. Additives such as nanotubes, whiskers, fibers, and nanoclusters have been shown to enhance the properties of these hybrid materials, and their use in small fractions may enhance the overall performance of the dental resin materials.

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 score0.018
Threshold uncertainty score0.512

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 communication0.0010.000
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 head0.018
GPT teacher head0.261
Teacher spread0.243 · 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