Sci‐PM Fri ‐ 02: Determination of optical properties of tissue <i>in situ</i> using four different frequency domain models
Bibliographic record
Abstract
The following problem was investigated: implant an intensity modulated light source and two detectors in tissue. From a measurement of the ratio of fluences and relative phase at the two detectors, estimate the absorption (μa) and reduced scattering (μs′) coefficients of the tissue. An initial guess of these quantities is iteratively improved by comparison of forward calculations with measurements. The model used for forward calculation must be fast, yet accurate. Four models were compared on the basis of errors in recovered optical properties: standard diffusion approximation (SDA), single Monte Carlo (SMC), delta‐P1, and isotropic similarity model (ISM). For μs′/μa>10, SDA recovered both coefficients within 5–10%, but for lower μs′/μa the errors were as large as 25%. Although the delta‐P1 model has been found to perform better under some conditions, we found no significant advantage in this application. The ISM was equivalent to SDA for μs′/μa>10 and gave errors in μa less than 7% and μs′ less than 17% for low albedo. The SMC model was the best; both coefficients were recovered to within 10% regardless of the albedo. In order to apply the SMC model, a restricted optical coefficient space must be used in the inverse problem solution.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".