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Record W2042100435 · doi:10.1088/0031-9155/46/10/301

Photothermal determination of optical coefficients of tissue phantoms using an optical fibre probe

2001· article· en· W2042100435 on OpenAlexfundno aff
Jan Laufer, Paul C. Beard, S.P. Walker, Tim Mills

Bibliographic record

VenuePhysics in Medicine and Biology · 2001
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsnot available
FundersAtomic Energy of Canada LimitedSwedish Foundation for International Cooperation in Research and Higher Education
KeywordsMaterials sciencePhotothermal therapyOpticsLaserOptical fiberAbsorption (acoustics)ScatteringAttenuation coefficientPhotothermal effectInterferometryFinesseFabry–Pérot interferometerOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

The absorption and reduced scattering coefficients of turbid tissue phantoms have been determined from photothermal measurements made using an optical fibre probe. The thermal sensor was a thin polymer film positioned at the end of a multimode optical fibre. The film was illuminated by the output of a continuous-wave diode laser and formed the cavity of a low-finesse Fabry-Perot interferometer. Low energy laser pulses, launched into the fibre and passed through the film, produced an abrupt temperature rise in the target tissue, which was placed in contact with the film. The subsequent conduction of heat into the film caused a change in its optical thickness and hence the reflected intensity. The absorption and reduced scattering coefficients of gelatine tissue phantoms of known optical properties were determined from the measurements using a numerical model of photothermal signal generation and maximum a posteriori estimation. The determined optical coefficients were in good agreement with the known values. The results showed that the probe can be used for the determination of optical coefficients provided the thermal coefficients of the target tissue are known with low uncertainty.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.084
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

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

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 head0.146
GPT teacher head0.454
Teacher spread0.308 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2001
Admission routes1
Has abstractyes

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