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Record W4280560541 · doi:10.1364/josab.459508

Characterization of Giles parameters for extended L-band erbium-doped fibers

2022· article· en· W4280560541 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the Optical Society of America B · 2022
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsHuawei Technologies (Canada)Université Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCharacterization (materials science)ErbiumMaterials scienceErbium doped fiber amplifierDopingAttenuation coefficientComputational physicsFiberNoise (video)Optical amplifierOpticsAbsorption (acoustics)Noise figureAmplifierWavelength-division multiplexingMeasurement uncertaintyPhysicsOptoelectronicsComputer scienceStatisticsMathematicsWavelength

Abstract

fetched live from OpenAlex

In this study, we present theoretical and experimental uncertainty analysis of erbium-doped fiber (EDF) characterization to improve performance prediction of erbium-doped fiber amplifiers (EDFAs) in the extended L-band. Through this uncertainty analysis, the optimal EDF lengths for absorption coefficient and emission coefficient characterization are determined to improve precision with a limited number of experimental steps. The uncertainty in the measured clustering ratio is also evaluated based on the uncertainty analysis of absorption and emission coefficients. To verify the accuracy of the Giles parameters determined from the EDF characterization, we compare simulation results, with calculated upper and lower uncertainties for the gain and noise figure (NF), to experimental measurements. The results show that the measured spectral gain and NF match well with the calculated value.

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)0.000
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 score0.276
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.211
Teacher spread0.202 · 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