Bibliographic record
Abstract
Purpose: The amblyopic visual system exhibits both positional uncertainty and distortion. Animals whose visual input in early life has been disrupted also exhibit severe deficits in positional coding (Gingras 2005). We studied the quality of the retinotopic map by measuring the perceived position of stimuli presented to various parts of the amblyopic visual field. Methods: Using a polarization method, we have tested 15 amblyopes and 5 normals. The stimuli were Gaussian blobs, which were presented within a circle of 30 degrees diameter. Each blob was seen only by the amblyopic eye. Moving a mouse and a marker seen only by the fellow-fixing eye, each subject had to localize the position of this previously presented target. This was repeated 50 times in each of 32 field positions. Refraction and alignment of the eyes were corrected before data collection. Results: Our results confirm previous findings that there are significant degrees of distortion in the maps of the central visual field in amblyopic subjects (Fronius 1989). However, the variability was not correlated with the measured distortion in amblyopic maps. The distortion/variability index was significantly larger in amblyopic maps, showing that the higher distortion in amblyopia could not be simply explained by higher variability in localization. Also, regional analysis of the data showed that the distortion occurred heterogeneously in different parts of the visual field. Conclusions: Our results show that amblyopes are not only uncertain as to where objects are but also they experience stable distortions that may only affect circumscribed regions of the visual field.
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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".