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Record W2072695176 · doi:10.1364/ol.33.000657

Electrostatic forward-viewing scanning probe for Doppler optical coherence tomography using a dissipative polymer catheter

2008· article· en· W2072695176 on OpenAlexaff
Nigel R. Munce, Adrian Mariampillai, Beau A. Standish, Mihaela Pop, Kevan Anderson, George Y. Liu, Tim Luk, Brian K. Courtney, Graham A. Wright, I. Alex Vitkin, Victor X. D. Yang

Bibliographic record

VenueOptics Letters · 2008
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsOpticsOptical coherence tomographyDoppler effectDissipative systemCoherence (philosophical gambling strategy)TomographyPhysicsMaterials science

Abstract

fetched live from OpenAlex

A novel flexible scanning optical probe is constructed with a finely etched optical fiber strung through a platinum coil in the lumen of a dissipative polymer. The packaged probe is 2.2 mm in diameter with a rigid length of 6mm when using a ball lens or 12 mm when scanning the fiber proximal to a gradient-index (GRIN) lens. Driven by constant high voltage (1-3 kV) at low current (< 5 microA), the probe oscillates to provide wide forward-viewing angle (13 degrees and 33 degrees with ball and GRIN lens designs, respectively) and high-frame-rate (10-140 fps) operation. Motion of the probe tip is observed with a high-speed camera and compared with theory. Optical coherence tomography (OCT) imaging with the probe is demonstrated with a wavelength-swept source laser. Images of an IR card as well as in vivo Doppler OCT images of a tadpole heart are presented. This optomechanical design offers a simple, inexpensive method to obtain a high-frame-rate forward-viewing scanning probe.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.252
Teacher spread0.228 · 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

Citations35
Published2008
Admission routes1
Has abstractyes

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