Depth profilometric case studies in caries diagnostics of human teeth using modulated laser radiometry and luminescence
Bibliographic record
Abstract
Simultaneous measurements from human teeth of photothermal radiometric (PTR) and luminescence (LM) signals induced by an intensity modulated laser have been performed to assess the feasibility of detecting deep lesions and near-surface cracks, to examine the effects of varying enamel thicknesses, the presence of fillings, and stains on the surface of teeth. A commercial dc luminescence monitoring instrument (DIAGNOdent by KaVo) was also used to examine a set of teeth for comparison purposes with PTR and LM. PTR amplitude signals from carious regions and from thin enamel were higher than those from healthy regions and thicker enamel. A crack produces a peak in the PTR amplitude scan, as well as a sudden change in the luminescence amplitude at the corresponding point. At low frequencies (5 Hz), the PTR amplitude showed high sensitivity to a deep (about 2 mm) lesion, while at high frequencies (700 Hz) it was more sensitive to surface cracks. It was concluded that by selecting proper modulation frequencies of the laser, measurements of PTR and LM signals could be used as a dental diagnostic technique with a small, inexpensive, low-power (<30 mW) semiconductor laser as a light source emitting in the optical window range of hard tissue (650–1000 nm).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".