“Emmetropic, but not myopic human eyes distinguish positive defocus from calculated defocus in monochromatic red light”
Bibliographic record
Abstract
Studies in animal models have provided evidence that broadband light and chromatic cues are necessary for successful emmetropization. We have studied this question in young human subjects by measuring short-term changes in axial length when they watched movies with calculated defocus (2.5D) or optically defocused movies (+2.5D) with red interference filters (620 ± 10 nm). Since filters cut luminance down by a factor of 10, a control experiment with neutral density filters (ND 1.0) was done. Ten myopes and 10 emmetropes were studied. Four experimental conditions were tested on two separate days. On the first day, movies with calculated defocus, and defocused by positive lenses were watched with ND filters. On the second day, movies with the same defocus patterns were watched with the red filters. Movies were presented on a large TV screen (LG OLED65C9, 65″) in a dark room at 2 m distance for 30 min. Changes in axial length before and after each stimulation were measured with the Lenstar (LS 900, with autopositioning system; Haag-Streit). Interestingly, the effects of calculated defocus or optical positive defocus on axial length were suppressed by 1.0 ND filters in myopes and emmetropes, with no clear trend. In contrast, narrow-band red light suppressed eye elongation with calculated defocus but not eye shortening with positive defocus in emmetropes. In myopes, as previously found in white light, there was a trend of axial eye elongation with positive lenses. In conclusion, the effect of positive lenses on eye growth did not require chromatic cues.
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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) | 0.000 | 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) | 0.002 | 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".