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Record W4400207977 · doi:10.1063/5.0212455

2 W monolithic fiber laser at 3.8 <i>µ</i>m

2024· article· en· W4400207977 on OpenAlexfundno aff
Maxime Lemieux-Tanguay, Tommy Boilard, Pascal Paradis, Réal Vallée, Martin Bernier

Bibliographic record

VenueAPL Photonics · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceFiber laserFiber Bragg gratingOpticsLaserDispersion-shifted fiberPolarization-maintaining optical fiberLaser power scalingSlope efficiencyOptoelectronicsFiberWavelengthFiber optic sensorPhysics

Abstract

fetched live from OpenAlex

We report a dual-wavelength-pumped all-fiber continuous-wave laser operating at the extended wavelength of 3.79 µm that reaches a record output power of 2.0 W. This represents, to the best of our knowledge, the highest output power reported at the longest spectral range for a fiber laser. The laser cavity, made of a heavily erbium-doped fluoride fiber and bounded by two photo-inscribed fiber Bragg gratings, reaches a slope efficiency of 46.5% with respect to the absorbed 1976 nm pump power. The system exhibits an absorption dependency of the 1976 nm pump on the launched 976 nm pump and a quenching behavior dependency on the output coupler reflectivity. The all-fiber design of the cavity allows significant power scaling of the laser and ensures its long-term stability.

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 communication0.0000.001
Open science0.0010.000
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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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

Citations13
Published2024
Admission routes1
Has abstractyes

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