Robust multiparameter method of evaluating the optical and thermal properties of a layered tissue structure using photothermal radiometry
Bibliographic record
Abstract
The thermal and optical properties of multilayered dental tissue structure, the result of the surface-grown prismless layer on enamel, were evaluated simultaneously using multiparameter fits of photothermal radiometry frequency responses. The photothermal field generated in a tooth sample with near-infrared laser excitation was described using a coupled diffuse-photon-density and thermal wave model. The optical (absorption and scattering) coefficients and thermal parameters (spectrally averaged infrared emissivity, thermal diffusivity and conductivity) of each layer, as well as the thickness of the upper prismless enamel layer, were fitted using a multiparameter simplex downhill minimization algorithm. The results show that the proposed fitting approach can increase robustness of the multiparameter estimation of tissue properties in the case of ill-defined multiparameter fits, which are unavoidable in in vivo tissue evaluation. The described method can readily be used for noninvasive in vitro or in vivo characterization of a wide range of layered biological tissues.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".