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Record W1988990010 · doi:10.1167/9.6.12

Blur on the retina due to higher-order aberrations: Comparison of eye growth models to experimental data

2009· article· en· W1988990010 on OpenAlexafffund
Jennifer J. Hunter, Meghan C. Campbell, M. L. Kisilak, Elizabeth L. Irving

Bibliographic record

VenueJournal of Vision · 2009
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of Waterloo
FundersCanada Research Chairs
KeywordsRetinaMonochromatic colorConstant (computer programming)Contrast (vision)Aberrations of the eyeOpticsPupilPhysicsRetinalHuman eyeScalingMathematicsOphthalmologyComputer scienceGeometryVisual acuity

Abstract

fetched live from OpenAlex

In the simplest model of eye growth, the ocular optics uniformly scale upwards, as do monochromatic higher-order aberrations (HOA) and linear blur on the retina. However, measured HOA remain constant or decrease with growth in some species. A new model, which holds HOA and the associated linear blur on the retina constant, was used to predict changes in HOA and resulting image quality on the retina during growth, in each of chick, monkey, and human. Models used rates of growth in each of the three species. Angular optical quality on the retina due to HOA, and its metrics improved, in contrast to the constancy predicted by uniform scaling. The model with constant linear HOA blur predicts well the improvement in human optical quality between infant and adult. Overall, in chick and monkey, angular blur improves at a rate faster than that predicted by the constant linear blur model, implying that linear retinal blur due to HOA decreases with age. On the other hand, in chick, angular blur due to third-order aberrations decreased at a rate predicted by the constant linear blur model. Growth changes in retinal blur due to HOA are species dependent but can be better understood by comparison with the new model predictions.

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)0.000
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 score0.808
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.133
GPT teacher head0.459
Teacher spread0.326 · 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

Citations11
Published2009
Admission routes2
Has abstractyes

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