What Image Properties Regulate Eye Growth
Bibliographic record
Abstract
* This article is free to read on journal website. The growth of the eye, unlike other parts of the body, is not ballistic. It is guided by visual feedback with the eventual aim being optimal focus of the retinal image or emmetropization. It has been shown in animal models that interference with the quality of the retinal image leads to a disruption to the normal growth pattern, resulting in the development of refractive errors and defocused retinal images. While it is clear that retinal images rich in pattern information are needed to control eye growth, it is unclear what particular aspect of image structure is relevant. Retinal images comprise a range of spatial frequencies at different absolute and relative contrasts and in different degrees of spatial alignment. Here we show, by using synthetic images, that it is not the local edge structure produced by relative spatial frequency alignments within an image but rather the spatial frequency composition per se that is used to regulate the growth of the eye. Furthermore, it is the absolute energy at high spatial frequencies regardless of the spectral slope that is most effective. Neither result would be expected from currently accepted ideas of how human observers judge the degree of image blur in a scene where both phase alignment and the relative energy distribution across spatial frequency (i.e., spectral slope) are important.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".