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Record W2134136428 · doi:10.1117/1.jbo.18.5.056007

Adaptive optics optical coherence tomography for<i>in vivo</i>mouse retinal imaging

2013· article· en· W2134136428 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Biomedical Optics · 2013
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsSimon Fraser University
FundersNational Eye InstituteNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMichael Smith Health Research BCUniversity of California, DavisResearch to Prevent Blindness
KeywordsOptical coherence tomographyAdaptive opticsOpticsPreclinical imagingRetinaWavefrontLens (geology)RetinalPhysicsIn vivoOphthalmologyMedicineBiology

Abstract

fetched live from OpenAlex

Small animal models of retinal diseases are important to vision research, and noninvasive high resolution in vivo rodent retinal imaging is becoming an increasingly important tool used in this field. We present a custom Fourier domain optical coherence tomography (FD-OCT) instrument for high resolution imaging of mouse retina. In order to overcome aberrations in the mouse eye, we incorporated a commercial adaptive optics system into the sample arm of the refractive FD-OCT system. Additionally, a commercially available refraction canceling lens was used to reduce lower order aberrations and specular back-reflection from the cornea. Performance of the adaptive optics (AO) system for correcting residual wavefront aberration in the mice eyes is presented. Results of AO FD-OCT images of mouse retina acquired in vivo with and without AO correction are shown as well.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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