MétaCan
Menu
Back to cohort
Record W4229940227 · doi:10.1117/12.518780

<title><emph type="1">En-face </emph>optical coherence tomography</title>

2003· article· en· W4229940227 on OpenAlexaff
Adrian Podoleanu, Richard B. Rosen, John A. Rogers, George Dobre, Radu G. Cucu, David A. Jackson, Shane Dunne, Bennett T. Amaechi

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsNanoacademic Technologies
FundersEngineering and Physical Sciences Research CouncilResearch Councils UK
KeywordsOptical coherence tomographyFace (sociological concept)Computer scienceOpticsConfocalCoherence (philosophical gambling strategy)Instrumentation (computer programming)PhysicsSoftwareRetinaAdaptive opticsArtificial intelligenceComputer visionComputer graphics (images)

Abstract

fetched live from OpenAlex

A review is presented of the developments in Kent in the field of optical coherence tomography (OCT) based instrumentation. Original versatile imaging systems have been devised which allow operation in different regimes under software control. Using such systems, B-scan and C-scan images are demonstrated from retina, anterior chamber, skin and teeth. The systems developed in Kent employ the flying spot concept, i.e. they use en-face scanning of the beam across the target. This has opened the possibility of providing simultaneous en-face OCT and confocal images (C-scans). Application of a standalone OCT/confocal system for investigating the retina in eyes with pathology, the anterior chamber, skin and teeth is demonstrated.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0650.044

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 head0.010
GPT teacher head0.223
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 source (direct Gemma or distilled Codex), 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
Published2003
Admission routes1
Has abstractyes

Explore more

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