Estimation and compensation of dispersion for a high-resolution optical coherence tomography system
Bibliographic record
Abstract
Abstract Balanced reference-sample arm dispersion is critical in optical coherence tomography systems in order to attain images with the highest axial resolution. Here, an experimental method for the estimation and correction of dispersion in an optical coherence tomography system is presented. The system dispersion was computed from two optical coherence tomography images of the reference mirror that were symmetrically placed around the zero delay point. The method was tested using a broad bandwidth spectral domain optical coherence tomography system, compensating for the dispersion caused by a 3 mm thick fused silica flat placed in the sample arm. Using our method, dispersion compensation was achieved and the axial resolution was improved from 10.6 μ m to 1.9 μ m in air. Results suggest that this technique can be a simple and effective method for eliminating axial resolution degradation due to dispersion mismatch between the sample and reference arm in high-resolution optical coherence tomography systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".