Double-integrating-sphere system at the National Institute of Standards and Technology in support of measurement standards for the determination of optical properties of tissue-mimicking phantoms
Bibliographic record
Abstract
There is a need for a common reference point that will allow for the comparison of the optical properties of tissue-mimicking phantoms. After a brief review of the methods that have been used to measure the phantoms for a contextual backdrop to our approach, this paper reports on the establishment of a standardized double-integrating-sphere platform to measure absorption and reduced scattering coefficients of tissue-mimicking biomedical phantoms. The platform implements a user-friendly graphical user interface in which variations of experimental configurations and model-based analysis are implemented to compute the coefficients based on a modified inverse adding-doubling algorithm allowing a complete uncertainty evaluation. Repeatability and validation of the measurement results of solid phantoms are demonstrated for three samples of different thicknesses, d = 5.08 mm, 7.09 mm, and 9.92 mm, with an absolute error estimate of 4.0% to 5.0% for the absorption coefficient and 11% to 12% for the reduced scattering coefficient (k = 2). The results are in accordance with those provided by the manufacturer. Measurements with different polarization angles of the incident light are also presented, and the resulting optical properties were determined to be equivalent within the estimated uncertainties.
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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 | 0.043 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".