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Record W4307379156 · doi:10.1364/josaa.469734

Mathematical modeling and experimental verification of aging human eyes polarization sensitivity

2022· article· en· W4307379156 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Optical Society of America A · 2022
Typearticle
Languageen
FieldEngineering
TopicOptical Polarization and Ellipsometry
Canadian institutionsOptina Diagnostics (Canada)
FundersJilin Scientific and Technological Development ProgramHigher Education Discipline Innovation ProjectChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsPolarization (electrochemistry)PerceptionStokes parametersComputer scienceOptical transfer functionSpatial frequencyOpticsArtificial intelligenceComputer visionPhysicsPsychologyNeuroscience

Abstract

fetched live from OpenAlex

The polarization perception sensitivity of the human eyes affects the perceived polarized image quality. In this paper, we used polarized spatiotemporal structured images to develop a spatiotemporal age mapping of the polarization perception of human eyes. We built an optical modulation transfer function mathematical model of the aging human eyes with spatiotemporal frequency domains and introduced the Stokes vector to analyze the polarized images. The proposed model provides a testing method based on a set of polarization images with spatiotemporal frequencies varying according to the perception of differently aged viewers. Then, we experimentally validated the proposed model by performing polarization perception tests on a group of volunteers. The test method has the diagnostic potential to confirm the health of human eyes and identify potential age-related macular diseases.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.234

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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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