A Study of the Statistical Properties of Scattered Radiation under Optical Coherence Tomography Conditions: Scattering Medium Characterization via the Power Spectrum and the Probability Density Function
Bibliographic record
Abstract
This thesis explores theory and data processing techniques that have the potential to extract much more information about a sample than can be gathered from standard optical coherence tomography (OCT) structural images. First, work is done towards making non-invasive glucose measurements via OCT and dynamic light scattering (DLS) techniques. A recently developed theory which models the power spectrum of the radiation scattered from flowing Brownian particles under OCT conditions is described, along with the experiments conducted that validate it. Second, it is demonstrated that the scattering of radiation from tissue-simulating phantoms has non-Gaussian features when there are a small number of scatterers in the OCT scattering volume, and that the statistics are well-described by the K distribution, which allows for scatterer concentration measurements. Subsequent preliminary experiments were conducted on human tissue, and the results show promise for differentiating between different tissue types.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".