Photothermal determination of optical coefficients of tissue phantoms using an optical fibre probe
Bibliographic record
Abstract
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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".