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Optical Coherence Tomography: An Introduction to the Technique and its Use

2000· article· en· W7143741026 on OpenAlexaff
Patricia K. Hrynchak, T. Simpson

Bibliographic record

VenueOptometry and Vision Science · 2000
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOptical coherence tomographyRetinalRetinaOptic nerveAdaptive opticsReflectivityCoherence (philosophical gambling strategy)

Abstract

fetched live from OpenAlex

ABSTRACT: This report describes the new optical imaging technique of optical coherence tomography (OCT). OCT is capable of high‐resolution, micrometer‐scale, cross‐sectional imaging of biological tissue. The OCT for ophthalmic application uses 843‐nm, near‐infrared light, which produces a longitudinal resolution of 10 to 20 μm and a penetration depth of a few millimeters. The scans are displayed in a false color representation scale on which warm colors represent areas of high optical reflectivity and cool colors represent areas of minimal or no reflectivity. A cross‐sectional view similar to a histology section is obtained. The cornea, iris, and lens may be visualized as well as the retina and optic nerve. OCT has been used to investigate several ocular diseases. These include macular disease, genetic retinal disease, retinal detachment and retinoschisis, choroidal tumors, optic nerve disorders, and glaucoma.

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 score0.707
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.346
Teacher spread0.333 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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