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Record W2904166764 · doi:10.1364/boe.10.000252

Sensorless adaptive optics multimodal en-face small animal retinal imaging

2018· article· en· W2904166764 on OpenAlexafffund
Daniel J. Wahl, Ringo Ng, Myeong Jin Ju, Yifan Jian, Marinko V. Šarunic

Bibliographic record

VenueBiomedical Optics Express · 2018
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsSimon Fraser University
FundersMichael Smith Health Research BCCanadian Institutes of Health ResearchGenome British ColumbiaInnovate BCNatural Sciences and Engineering Research Council of CanadaFondation Brain Canada
KeywordsScanning laser ophthalmoscopyOptical coherence tomographyAdaptive opticsRetinaConfocalComputer scienceOpticsLens (geology)Physics

Abstract

fetched live from OpenAlex

retina imaging system that includes optical coherence tomography (OCT), OCT-angiography, confocal scanning laser ophthalmoscopy (SLO), and fluorescence detection. We present a compact lens-based imaging system design that allows for a 50-degree maximum field of view (FOV), which can be reduced to the region of interest to perform SAO with the modality of choice. The system performance was demonstrated on wild type mice (C57BL/6J), and transgenic mice with GFP labeled cells. SAO SLO was used for imaging microglia (Cx3cr1-GFP) over ~1 hour, where dynamics of the microglia branches were clearly observed. Our results also include volumetric cellular imaging of microglia throughout the inner retina.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.293
Teacher spread0.272 · 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

Citations51
Published2018
Admission routes2
Has abstractyes

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