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Record W4392397240 · doi:10.1109/jqe.2024.3372580

Modeling the 3-Micron Class Er-Doped Fluoride Fiber Laser With a Cubic Energy Transfer Rate Dependence

2024· article· en· W4392397240 on OpenAlexafffund
William H. Bisson, A. Michaud, Pascal Paradis, Réal Vallée, Martin Bernier

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGlass properties and applications
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMaterials scienceFiber laserDopingFluorideLaserOptoelectronicsEnergy transferOpticsFiberMolecular physicsComposite materialPhysicsChemistry

Abstract

fetched live from OpenAlex

We propose an energy transfer model with a cubic atomic population dependence to accurately model the behavior of various reported high-power erbium-doped fluoride fiber lasers operating near 2.8 microns. We first show that the previously introduced weakly interacting (WI) and strongly interacting (SI) models are not adequate for precisely modeling such high-power erbium-doped fluoride fiber lasers. We compare results obtained with the WI and SI models to the proposed model by simulating 4 different highly doped (7 mol.%) fiber lasers previously reported in the literature. Laser efficiencies and powers are reproduced with great accuracy. In addition, four other fiber laser systems based on erbium concentrations varying from 1–6 mol.% are also simulated with good accuracy using the proposed model with the exact same set of spectroscopic parameters, which confirms its validity for various erbium doping concentrations. Redshifting of laser wavelength is also taken into account by considering the full cross section spectra and computing signal powers over several wavelength channels.

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 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 score0.204
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.239
Teacher spread0.221 · 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 teacher head, 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

Citations4
Published2024
Admission routes2
Has abstractyes

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