Multi-factor modeling of OCT-scan formation in the presence of scatterer motions
Bibliographic record
Abstract
We present a robust semi-analytical multi-factor model of OCT-scan formation with rigorous accounting for the beam-focusing effects. It is based on numerical summation of the contributions of the localized sub-resolution scatterers, accounting for variations in the phase-amplitude parameters of the incident and backscattered optical waves, with a subsequent integration of the latter over the objective aperture. The main advantage of presented model is its convenience to account for scatterer motions in comparison with classical Monte-Carlo OCT-simulation approaches. In view of difficulties in performing highly-controlled phantom experiments, application of an adequate numerical model opens very flexible and convenient possibilities for evaluating the influence of scatter motions using numerically simulated OCT scans. The numerical model readily allows one to introduce various (e.g., regular/irregular or mixed) types of scatterer motions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".