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Record W2038970916 · doi:10.1364/boe.2.001268

In vivo volumetric imaging of chicken retina with ultrahigh-resolution spectral domain optical coherence tomography

2011· article· en· W2038970916 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

VenueBiomedical Optics Express · 2011
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRetinaOptical coherence tomographyRetinalPreclinical imagingImage resolutionGanglion cell layerTomographyOpticsMaterials scienceBiomedical engineeringIn vivoOphthalmologyMedicineBiologyPhysics

Abstract

fetched live from OpenAlex

The chicken retina is an established animal model for myopia and light-associated growth studies. It has a unique morphology: it is afoveate and avascular; oxygen and nutrition to the inner retina is delivered by a vascular tissue (pecten) that protrudes into the vitreous. Here we present, to the best of our knowledge, the first in vivo, volumetric high-resolution images of the chicken retina. Images were acquired with an ultrahigh-resolution optical coherence tomography (UHROCT) system with 3.5 µm axial resolution in the retina, at the rate of 47,000 A-scans/s. Spatial variations in the thickness of the nerve fiber and ganglion cell layers were mapped by segmenting and measuring the layer thickness with a semi-automatic segmentation algorithm. Volumetric visualization of the morphology and morphometric analysis of the chicken retina could aid significantly studies with chicken retinal models of ophthalmic 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 categoriesMeta-epidemiology (narrow)
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 score0.248
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
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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