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Record W1978441975 · doi:10.4012/dmj.28.523

Relationship between fluorescence loss of QLF and depth of demineralization in an enamel erosion model

2009· article· en· W1978441975 on OpenAlex
Keiko Nakata, Toru Nikaido, 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 · 2009
Typearticle
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsSt. Thomas Hospital
FundersTokyo Medical and Dental University
KeywordsDemineralizationEnamel paintMaterials scienceVarnishRemineralisationErosionDentistryComposite materialMedicineGeology

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the relationship between quantitative light-induced fluorescence (QLF) values and demineralization depths in an enamel erosion model in vitro. Flat labial enamel surfaces of bovine incisors were ground with 800-grit SiC and coated with nail varnish, but also leaving rectangular windows of enamel uncoated. Subsequently, they were immersed in a lactic acid gel (pH 5.0) for 0 to 7 weeks to make an enamel erosion model. Carious lesions thus induced were analyzed by QLF and the demineralization depths measured using SEM/ EDS method at the end of each period. A wide range of erosive lesions were produced with a steady increase in both demineralizing depth and fluorescence loss (DeltaF) over time. With this model, a good correlation was exhibited between each DeltaF value and the demineralization depth. Results of this study indicated that QLF could detect and quantify mineral loss under the eroded surface of the enamel erosion model.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.382

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 head0.044
GPT teacher head0.322
Teacher spread0.278 · 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