Polarization-sensitive OCT system using single-mode fiber
Bibliographic record
Abstract
Polarization sensitive optical coherence tomography (PS-OCT) takes into account the vector nature of light waves (state of polarization). The most complete information about the polarization properties of a biological target is given by the depth resolved Mueller matrix elements, however, it is difficult to construct such a system for in-vivo examination. We designed and assembled a simpler system, with two incoherent channels to provide limited information, but essential on the polarization properties of the tissue. The interferometer is a hybrid configuration of bulk optic and single mode optical fiber components. No polarization maintaining fiber is used. The reference and sample beams interfere in single mode optical couplers. The low coherence light source is a superluminescent diode of 850 nm center wavelength and 25 nm spectrum FWHM (which corresponds to a depth resolution of 12 microns in tissue). The system can display either a pair of two polarisation sensitive OCT images, corresponding to linear orthogonal polarisation directions or a pair of images, a polarisation insensitive (pure reflectivity) image and a birefringence retardation map. The 12 bit grayscale images are collected by fast en-face scanning (T-scan) at 2 frames/s. We demonstrate in vivo en face images of the retinal nerve fiber layer, lamina cribrosa, cornea and teeth. A rotation angle of 0.3 degrees per micron was evaluated from the retinal nerve fiber layer and lamina cribrosa.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".