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Record W2125125117 · doi:10.4236/jbise.2010.31004

Fiber lenses for ultra-small probes used in optical coherent tomography

2010· article· en· W2125125117 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.

Bibliographic record

VenueJournal of Biomedical Science and Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsNational Research Council CanadaInstitute for Microstructural Sciences
Fundersnot available
KeywordsOptical coherence tomographyMaterials scienceOpticsLens (geology)Optical fiberGradient-index opticsFiberFabricationOptoelectronicsRefractive indexPhysics

Abstract

fetched live from OpenAlex

We present a design, construction and characterization of different variations of GRIN and ball fiber lenses, which were recently proposed for ultra-small biomedical imaging probes. Those fiber lens modules are made of a single mode fiber and a GRIN or ball fiber lens with or without a fiber spacer between them. The lens diameters are smaller than 0.3 mm. We discuss design methods, fabrication techniques, and measuring performance of the fiber lenses. The experimental results are compared to their modeling results. The fabrication of a high quality beam director for both lens types is presented as well. These fiber integrated beam directors could be added on the tips of the fiber lenses for side-view probes. A needle probe made by these fiber lenses is demonstrated as a sample of the ultra-small probe for biomedical imaging application. In vivo human finger images acquired by a swept source optical coherence tomography using the fiber lenses with different beam profiles were shown, which indicates the important impact of fiber

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)0.001
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 score0.541
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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