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Record W2002918884 · doi:10.1080/09500340.2013.826390

A note on image degradation, disability glare, and binocular vision

2013· article· en· W2002918884 on OpenAlexaff
Vandana Rajaram, Vasudevan Lakshminarayanan

Bibliographic record

VenueJournal of Modern Optics · 2013
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGLARELuminanceStereoscopyContrast (vision)StereopsisBinocular visionComputer visionBinocular disparityComputer scienceArtificial intelligencePerceptionOpticsOptometryPsychologyPhysicsMaterials scienceMedicine

Abstract

fetched live from OpenAlex

Disability glare due to scattering of light causes a reduction in visual performance due to a luminous veil over the scene. This causes problem such as contrast detection. In this note, we report a study of the effect of this veiling luminance on human stereoscopic vision. We measured the effect of glare on the horopter measured using the apparent fronto-parallel plane (AFPP) criterion. The empirical longitudinal horopter measured using the AFPP criterion was analyzed using the so-called analytic plot. The analytic plot parameters were used for quantitative measurement of binocular vision. Image degradation plays a major effect on binocular vision as measured by the horopter. Under the conditions tested, it appears that if vision is sufficiently degraded then the addition of disability glare does not seem to significantly cause any further compromise in depth perception as measured by the horopter.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.330
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2013
Admission routes1
Has abstractyes

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