Quantitative dental measurements by use of simultaneous frequency-domain laser infrared photothermal radiometry and luminescence
Bibliographic record
Abstract
Modulated (frequency-domain) infrared photothermal radiometry (PTR) is used as a dynamic quantitative dental inspection tool complementary to modulated luminescence (LM) to quantify sound enamel or dentin. A dynamic high-spatial-resolution experimental imaging setup, which can provide simultaneous measurements of laser-induced modulated PTR and LM signals from defects in teeth, has been developed. Following optical absorption of laser photons, the experimental setup can monitor simultaneously and independently the nonradiative (optical-to-thermal) energy conversion by infrared PTR and the radiative deexcitation by LM emission. The relaxation lifetimes (tau1, tau2) and optical absorption, scattering, and spectrally averaged infrared emission coefficients (mu(alpha), mu(s), mu(IR)) of enamel are then determined with realistic three-dimensional LM and photothermal models for turbid media followed by multiparameter fits to the data. A quantitative band of values for healthy enamel with respect to these parameters can be generated so as to provide an explicit criterion for the assessment of healthy enamel and, in a future extension, to facilitate the diagnosis of the onset of demineralization in carious enamel.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".