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The Theory of Object and Image Eccentricities: a New Dimension in Ophthalmic Optics

2003· article· en· W2069328214 on OpenAlexaff
Arnulf Remole

Bibliographic record

VenueOptometry and Vision Science · 2003
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer visionMagnificationImage planeArtificial intelligenceComputer scienceObject (grammar)Ray tracing (physics)MathematicsOpticsImage (mathematics)Physics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.394
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2003
Admission routes1
Has abstractyes

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