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Record W2043055045 · doi:10.1109/jphot.2014.2321754

Characterization of Optical Coherence Tomography Images Acquired at Large Distances With Large-Diameter Beams

2014· article· en· W2043055045 on OpenAlexaff
Dan P. Popescu, Michael S. Smith, Michael G. Sowa

Bibliographic record

VenueIEEE photonics journal · 2014
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsMedical Council of Canada
Fundersnot available
KeywordsCollimated lightOpticsOptical coherence tomographyCollimatorSample (material)Beam (structure)Materials sciencePhysicsLaser

Abstract

fetched live from OpenAlex

We tested the imaging capabilities for variants of a 1550-nm swept-source fiber-based optical coherence tomography system with a telecentric system incorporated at the end of its sample arm. The system was designed for in vivo imaging of burns; therefore, we acquired images from samples located at distances greater than 24 cm from the exit of the telecentric system. Each system variation had a specific combination of diameters for the reference and sample beams. In the reference arm, we used, alternately, two collimated beams with diameters of 1.5 and 14 mm, respectively. In the sample arm, we tested collimated beams with the following diameters: 1.5, 3.5, 5.7, 8.4, and 14 mm. A galvanometric mirror system scanned the collimated sample beam across the entrance pupil of the telecentric system. The sample beam exited the telecentric system parallel with its optical axis and convergent onto the sample. Depending on the collimator used in the sample arm, images were acquired with beams focused to waist diameters ranging from 40 to 240 μm. We acquired images with the sample at different locations within a ±30 mm range centered about the sample beam waist. Furthermore, we used the signal-to-noise ratio, the detected signal intensity, and the visual appearance to compare images acquired with different sample/reference beam configurations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.213
Teacher spread0.207 · 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 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

Citations2
Published2014
Admission routes1
Has abstractyes

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