Relationship between fluorescence loss of QLF and depth of demineralization in an enamel erosion model
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".