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Record W2963453704 · doi:10.1109/jphot.2019.2929077

Continuous-Wave Pumping Supercontinuum Generation in Random Distributed Feedback Laser Cavity

2019· article· en· W2963453704 on OpenAlexfundno aff
K. Y. Lau, F. H. Suhailin, N.H. Zainol Abidin, Ahmad Fauzi Abas, Mohammed T. Alresheedi, Mohd Adzir Mahdi

Bibliographic record

VenueIEEE photonics journal · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsnot available
FundersInstitute of Population and Public HealthKing Saud University
KeywordsSupercontinuumLasing thresholdOpticsMaterials scienceFiber laserLaserDistributed feedback laserOptical cavityActive laser mediumRaman scatteringOptoelectronicsLaser power scalingGain-switchingRaman laserWavelengthRaman spectroscopyPhysicsPhotonic-crystal fiber

Abstract

fetched live from OpenAlex

In this paper, we demonstrated a continuous-wave pumping supercontinuum generation in a random distributed feedback fiber laser with a completely open laser cavity. A broadband wavelength conversion was obtained by pumping a 36-km long TrueWaveREACH fiber in anomalous dispersion regime with a high power continuous-wave Raman laser. The spectral broadening was assisted via nonlinear mechanism such as modulation instability and stimulated Raman scattering. An extended 10-dB flat supercontinuum with 129-nm bandwidth spanning over C-, L-, and U-band wavelengths was generated in the forward direction of lasing cavity under 3.65-W pump power. The super-continuum exhibited excellent bandwidth stability in 60 minutes of lasing operation. A simultaneous generation of random Raman laser operating in the backward direction of cavity was also demonstrated within the same gain fiber. The simple laser cavity presented significant versatility in its generation of novel light sources for both telecommunication and applied science applications.

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 score0.002

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 science0.0010.000
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 head0.014
GPT teacher head0.202
Teacher spread0.188 · 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
Published2019
Admission routes1
Has abstractyes

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