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Record W4233378696 · doi:10.21611/qirt.2010.085

Quantitative analysis of dental tissue properties using photothermal radiometry

2010· article· en· W4233378696 on OpenAlexaff
A. Matvienko, A. Mandelis, A. Hellen, R. Jeon, Stephen H. Abrams, B.T. Amaechi

Bibliographic record

VenueProceedings of the 2010 International Conference on Quantitative InfraRed Thermography · 2010
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsXanadu Quantum Technologies (Canada)University of Toronto
Fundersnot available
KeywordsRadiometryPhotothermal therapyMaterials scienceRemote sensingEnvironmental scienceGeologyNanotechnology

Abstract

fetched live from OpenAlex

Photothermal Radiometry was applied as a safe and highly sensitive tool for the detection of early tooth enamel demineralization. An extracted human tooth was treated sequentially with an artificial demineralization gel to simulate controlled mineral loss in the enamel. A semiconductor laser was a source of infrared blackbody radiation from teeth upon absorption and non-radiative energy conversion. A coupled diffuse-photon-density-wave and thermal-wave theoretical model was developed to describe the biothermophotonic phenomena in multi-layered hard tissue structures. As a result of the data analysis, thermal and optical properties of sound and demineralized enamel were obtained.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.062
GPT teacher head0.337
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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