MétaCan
Menu
Back to cohort
Record W4403042769 · doi:10.3390/ma17194843

Opalescence and Fluorescence of 46 Resin-Based Composites Exposed to Ultraviolet Light

2024· article· en· W4403042769 on OpenAlexafffund
Soheil Ghaffari, A. Gulati, Richard Bengt Price

Bibliographic record

VenueMaterials · 2024
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsOpalescenceEnamel paintDentinFluorescenceMaterials scienceUltravioletComposite materialUltraviolet lightOpticsOptoelectronics

Abstract

fetched live from OpenAlex

Identifying the boundary between the tooth and the resin-based composite (RBC) is difficult when replacing restorations. Ultraviolet (UV) light has been reported to assist the viewer by causing the RBC to fluoresce. Using a laboratory-grade fiberoptic spectrometer, 46 RBCs were exposed to UV light from the Woodpecker O-Star curing light. The opalescence and fluorescence were measured relative to a human tooth that contained just dentin and a tooth that contained both enamel and dentin. After these quantitative measurements, 10 RBCs with large differences in light emittance were compared qualitatively to assess their brightness when exposed to UV light compared to the dentin specimen and the specimen containing both enamel and dentin. It was found that, when exposed to UV light, some of the RBCs were less bright compared to the two samples of teeth used for comparison, but most were brighter; some were up to six times brighter. The filler appears to affect the opalescence peaks, while the resin appears to affect the fluorescence peaks. It was concluded that because RBCs emit very different levels of opalescence and fluorescence, UV light from the Woodpecker O-Star cannot be relied upon to detect all brands of RBC on the tooth. The opalescence and fluorescence can also be used to detect changes in the formulation of the RBC.

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 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 score0.450

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 head0.013
GPT teacher head0.264
Teacher spread0.250 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueMaterialsSame topicDental materials and restorationsFrench-language works237,207