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Record W2020753081 · doi:10.1117/12.808544

Modelling the optical response of human retinal photoreceptors to plane wave illumination with the finite integration technique

2009· article· en· W2020753081 on OpenAlexafffund
Alireza Akhlagh Moayed, Shannon Dang, Omar M. Ramahi, Kostadinka Bizheva

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRetinalRetinaOpticsPhysicsOphthalmologyMedicine

Abstract

fetched live from OpenAlex

The early stages of ocular diseases such as Diabetic Retinopathy are manifested by morphological changes in retinal tissue occurring on cellular level. Therefore, a number of ophthalmic diseases can be diagnosed at an early stage by detecting spatial and temporal variations in the scattering profile of retinal tissue. It was recently demonstrated that, OCT can be used to probe the functional response of retinal photoreceptors to external light stimulation [1]-[3]. fUHROCT measures localized differential changes in the retina reflectivity over time resulting from external light stimulation of the retina. Currently the origins of the observed reflectivity changes are not well understood. However, due to the complex nature of retinal physiology using purely experimental approaches in this case is problematic. For example fUHROCT is sensitive to small changes in the refractive index of biological tissue which as demonstrated previously, can result from a number of processes such as membrane hyperpolarization, osmotic swelling, metabolic changes, etc. In this paper, we present a computational model of interaction between photoreceptor cells and optical plane wave based on the Finite Integration Technique (FIT).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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 designBench or experimental
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
Published2009
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Coherence Tomography ApplicationsFrench-language works237,207