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Record W2080285433 · doi:10.1364/ol.38.005208

Are the fluctuations in dynamic anterior surface aberrations of the human eye chaotic?

2013· article· en· W2080285433 on OpenAlexaff
Varadharajan Jayakumar, Damber Thapa, Natalie Hutchings, Vasudevan Lakshminarayanan

Bibliographic record

VenueOptics Letters · 2013
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsZernike polynomialsOpticsMeasure (data warehouse)BreakupChaoticLyapunov exponentMathematicsPhysicsOphthalmologyWavefrontComputer scienceMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of the study is to measure chaos in dynamic anterior surface aberrations and examine how it varies between the eyes of an individual. Noninvasive tear breakup time and dynamic corneal surface aberrations were measured for two open-eye intervals of 15 s. The maximal Lyapunov exponent (MLE) was calculated to test the nature of the fluctuations of the dynamic anterior surface aberrations. The average MLE for total higher-order aberration (HOA) was found to be small (+0.0102±0.0072) μm/s. No significant difference in MLE was found between the eyes for HOA (t-test; p=0.131). Data analysis was carried out for individual Zernike coefficients, including vertical prism as it gives a direct measure of the thickness of the tear film over time. The results show that the amount of chaos was small for each Zernike coefficient and not significantly correlated between the eyes.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0010.000

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 head0.034
GPT teacher head0.308
Teacher spread0.273 · 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 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

Citations1
Published2013
Admission routes1
Has abstractyes

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