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Record W4295096973 · doi:10.1364/ol.468707

Image-based cross-calibration method for multiple spectrometer-based OCT

2022· article· en· W4295096973 on OpenAlexfundno aff
Yusi Miao, Jun Song, Myeong Jin Ju

Bibliographic record

VenueOptics Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaAlzheimer Society Research ProgramCanada Foundation for Innovation
KeywordsOpticsSpectrometerOptical coherence tomographyCalibrationCoherence (philosophical gambling strategy)PixelImaging spectrometerCross-correlationPhysicsWavelengthComputer scienceMathematics

Abstract

fetched live from OpenAlex

A fast and practical computational cross-calibration of multiple spectrometers is described. A signal correlation matrix (CM) can be constructed from paired B-scans in a multiple-spectrometer optical coherence tomography (OCT), where the wavelength-corresponding pixels are indicated by high cross correlation. The CM can be used to either guide the physical alignment of spectrometers or to numerically match the spectra in the post-process. The performance is comparable to the previously reported optimization approach, as demonstrated by the mirror tests, qualitative comparison of OCT and optical coherence tomography angiography (OCTA) images, and quantitative comparison of image metrics.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.455
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.015
GPT teacher head0.272
Teacher spread0.257 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations7
Published2022
Admission routes1
Has abstractyes

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