The Theory of Object and Image Eccentricities: a New Dimension in Ophthalmic Optics
Bibliographic record
Abstract
During off-axis viewing through an anisometropic spectacle correction, the two eyes must diverge to fuse. This can cause great discomfort, especially in the vertical meridian. To consider a correction for the problem, one must know the amount of forced divergence induced by the spectacles. Because the eyes select ray entry points that do not coincide with the object point projections on the lens planes, the exact directions of gaze are difficult to determine with conventional ray-tracing methods. The methods previously developed for determining the visual directions of gaze through anisometropic spectacle corrections are limited to hypothetical thin lenses and require complicated trigonometric constructs that are not suitable for clinical work. A recently developed method for solving the problem applies the dynamic spectacle magnification to find the visual direction of an image of a given object point. The method, referred to as the theory of object and image eccentricities, is based on projections of object and image from the rotation center onto a common plane, such as the back vertex plane. The theory can be applied to many situations in ophthalmic optics previously difficult to analyze. It does not require ray tracing, and because it considers base curves and thickness, it is more accurate than previous methods. A major advantage is that it considers the two eyes as an integrated system rather than separately and in isolation from each other.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".