Standardized Full-Field Electroretinography in the Green Monkey (Chlorocebus sabaeus)
Bibliographic record
Abstract
Full-field electroretinography is an objective measure of retinal function, serving as an important diagnostic clinical tool in ophthalmology for evaluating the integrity of the retina. Given the similarity between the anatomy and physiology of the human and Green Monkey eyes, this species has increasingly become a favorable non-human primate model for assessing ocular defects in humans. To test this model, we obtained full-field electroretinographic recordings (ERG) and normal values for standard responses required by the International Society for Clinical Electrophysiology of Vision (ISCEV). Photopic and scotopic ERG recordings were obtained by full-field stimulation over a range of 6 log units of intensity in dark-adapted or light-adapted eyes of adult Green Monkeys (Chlorocebus sabaeus). Intensity, duration, and interval of light stimuli were varied separately. Reproducible values of amplitude and latency were obtained for the a- and b-waves, under well-controlled adaptation and stimulus conditions; the i-wave was also easily identifiable and separated from the a-b-wave complex in the photopic ERG. The recordings obtained in the healthy Green Monkey matched very well with those in humans and other non-human primate species (Macaca mulatta and Macaca fascicularis). These results validate the Green Monkey as an excellent non-human primate model, with potential to serve for testing retinal function following various manipulations such as visual deprivation or drug evaluation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".