MétaCan
Menu
Back to cohort
Record W2071484675 · doi:10.1117/12.842720

Hybrid Fourier domain modelocked laser utilizing a fiber optical parametric amplifier and an erbium doped fiber amplifier

2010· article· en· W2071484675 on OpenAlexaff
Kyle H. Y. Cheng, Beau A. Standish, Victor X. D. Yang, Kim K. Y. Cheung, Xijia Gu, Edmund Y. Lam, Kenneth K. Y. Wong

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreToronto Metropolitan University
Fundersnot available
KeywordsOptical amplifierOpticsMaterials scienceAmplifierFiber Bragg gratingWavelength-division multiplexingFiber laserOptical coherence tomographyOptical parametric amplifierOptoelectronicsLaserWavelengthPhysicsCMOS

Abstract

fetched live from OpenAlex

To our knowledge, we report the first Fourier domain modelocked laser (FDML) constructed using optical parameter amplifier (OPA) in conjunction with an erbium-doped fiber amplifier (EDFA), centered at ~1556nm. We utilized a onepump OPA and a C-band EDFA in a series configuration with a polygon-grating wavelength filter to generate a hybrid FDML spectrum. Results demonstrate a substantially higher output power, better spectral shape and significantly more stable bandwidth than individual configurations. We believe this technique has the potential to enable several amplifiers to complement individual deficiencies resulting in improved spectral shapes and power generation for imaging applications such as optical coherence tomography (OCT).

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 score0.004

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.013
GPT teacher head0.238
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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Coherence Tomography ApplicationsFrench-language works237,207