Optimization of data processing with the Akinetic swept-laser: algorithm to automatically adjust the A-scan synchronization delay
Bibliographic record
Abstract
The Akinetic swept-laser (Insight, Lafayette, USA) is an all-semiconductor compact and tunable laser source for optical coherence tomography (OCT). Wavelength sweeps with this source are composed of valid data sections interleaved with invalid data sections. The source provides a precise trigger for each wavelength sweep and a “data valid vector” (DVV) file which identifies the indices of the valid data points in a recorded interferogram. In order to identify valid data in real time during acquisition, a delay must be precisely adjusted between the trigger and the wavelength sweep. Optimizing this delay becomes tedious when changing the interferometer configuration in a multi-purpose OCT system. The source provides tools to do this, but they are not automated and require a sample with a single clean reflection. We developed a simple and robust algorithm, integrated in our OCT data acquisition and treatment software, for finding the optimal delay correction that must be applied to accurately identify the valid data obtained with the Akinetic swept-laser. It can perform optimization either from the laser spectrum or from an interferogram and facilitates delay readjustment when the interferometer configuration is modified.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".