Modelling the optical response of human retinal photoreceptors to plane wave illumination with the finite integration technique
Bibliographic record
Abstract
The early stages of ocular diseases such as Diabetic Retinopathy are manifested by morphological changes in retinal tissue occurring on cellular level. Therefore, a number of ophthalmic diseases can be diagnosed at an early stage by detecting spatial and temporal variations in the scattering profile of retinal tissue. It was recently demonstrated that, OCT can be used to probe the functional response of retinal photoreceptors to external light stimulation [1]-[3]. fUHROCT measures localized differential changes in the retina reflectivity over time resulting from external light stimulation of the retina. Currently the origins of the observed reflectivity changes are not well understood. However, due to the complex nature of retinal physiology using purely experimental approaches in this case is problematic. For example fUHROCT is sensitive to small changes in the refractive index of biological tissue which as demonstrated previously, can result from a number of processes such as membrane hyperpolarization, osmotic swelling, metabolic changes, etc. In this paper, we present a computational model of interaction between photoreceptor cells and optical plane wave based on the Finite Integration Technique (FIT).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".