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Record W1982460442 · doi:10.1364/josaa.24.001284

Characterizing image quality in a scanning laser ophthalmoscope with differing pinholes and induced scattered light

2007· article· en· W1982460442 on OpenAlexafffund
Jennifer J. Hunter, Christopher J. Cookson, M. L. Kisilak, Juan M. Bueno, Melanie C. W. Campbell

Bibliographic record

VenueJournal of the Optical Society of America A · 2007
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of Waterloo
FundersOntario Centres of Excellence
KeywordsOpticsImage qualityMaterials scienceLaserLaser lightLaser scanningQuality (philosophy)PhysicsImage (mathematics)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We quantify the effects on scanning laser ophthalmoscope image quality of controlled amounts of scattered light, confocal pinhole diameter, and age. Optical volumes through the optic nerve head were recorded for a range of pinhole sizes in 12 subjects (19-64 years). The usefulness of various overall metrics in quantifying the changes in fundus image quality is assessed. For registered and averaged images, we calculated signal-to-noise ratio, entropy, and acutance. Entropy was best able to distinguish differing image quality. The optimum confocal pinhole diameter was found to be 50 microm (on the retina), providing improved axial resolution and image quality under all conditions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.030
GPT teacher head0.352
Teacher spread0.322 · 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

Citations22
Published2007
Admission routes2
Has abstractyes

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