Novel dental dynamic depth profilometric imaging using simultaneous frequency-domain infrared photothermal radiometry and laser luminescence
Bibliographic record
Abstract
A high-spatial-resolution dynamic experimental imaging setup, which can provide simultaneous measurements of laser-induced frequency-domain infrared photothermal radiometric and luminescence signals from defects in teeth, has been developed for the first time. The major findings of this work are (i) radiometric images are complementary to (anticorrelated with) luminescence images, as a result of the nature of the two physical signal generation processes; (ii) the radiometric amplitude exhibits much superior dynamic (signal resolution) range to luminescence in distinguishing between intact and cracked sub-surface structures in the enamel; (iii) the radiometric signal (amplitude and phase) produces dental images with much better defect localization, delineation, and resolution; (iv) radiometric images (amplitude and phase) at a fixed modulation frequency are depth profilometric, whereas luminescence images are not; and (v) luminescence frequency responses from enamel and hydroxyapatite exhibit two relaxation lifetimes, the longer of which (approximately ms) is common to all and is not sensitive to the defect state and overall quality of the enamel. Simultaneous radiometric and luminescence frequency scans for the purpose of depth profiling were performed and a quantitative theoretical two-lifetime rate model of dental luminescence was advanced.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".