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Record W2063673370 · doi:10.1049/iet-opt:20070027

Broadly tuneable, short external cavity diode laser for optical coherence tomography

2008· article· en· W2063673370 on OpenAlexafffund
J. Wang, Daniel T. Cassidy

Bibliographic record

VenueIET Optoelectronics · 2008
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsOptical coherence tomographyLaserMaterials scienceOpticsSuperluminescent diodeDiodeCoherence (philosophical gambling strategy)Resolution (logic)OptoelectronicsLaser diodeTime domainOptical tomographyPhysicsComputer science

Abstract

fetched live from OpenAlex

The application of a broadly tuneable asymmetric multiple-quantum-well (AMQW) laser is demonstrated in an external cavity for both time-domain and Fourier-domain optical coherence tomography (OCT). The AMQW laser was custom designed and fabricated to have a large tuning range when operated with no facet coatings in a diffractive optical element (DOE) short external cavity (SXC). The depth resolution of Fourier domain OCT was found to be as short as 13 µm in air with the 117 nm tuning range (full-width-half-maximum of 80 nm) of the AMQW DOE SXC source. For time domain OCT, synthesised OCT with the AMQW sources was investigated. It was found that the depth resolution is set by the tuning range of the AMQW DOE SXC laser. The depth resolution can be improved as 117 nm is not the limit to the tuning range of AMQW lasers.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations9
Published2008
Admission routes2
Has abstractyes

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