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All Soft Glass Fiber Components and Sources

2023· article· en· W4386414197 on OpenAlexaffabout
Martin Rochette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsMcGill University
Fundersnot available
KeywordsOptical fiberOptoelectronicsMaterials sciencePolarization-maintaining optical fiberOpticsMultiplexerChalcogenide glassDispersion-shifted fiberSingle-mode optical fiberGraded-index fiberChalcogenideFiber optic sensorComputer scienceMultiplexingPhysicsTelecommunications

Abstract

fetched live from OpenAlex

The fabrication of fiber lasers that emit in mid-infrared wavelengths (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2-20\ \upmu \mathrm{m}$</tex>) require fiber-based building blocks compatible with this spectral range. Such fiber components include the optical fiber itself that must be transparent to the mid-infrared, also power combiners/dividers, wavelength division multiplexers/demultiplexers, polarization-dependent couplers, gain media, etc. This presentation highlights the recent progresses of the Nonlinear Photonics Group at McGill University, with an emphasis on all-fiber components that have been built from chalcogenide and fluoride fibers. These glasses have been specifically chosen for their optical properties such as transparency in the mid-infrared, high nonlinearity, and tolerance to high optical intensities. From those glasses, components and sources such as the first single mode optical fiber couplers (power dividers, wavelength-dependent, polarization dependent) as well as all soft-glass fiber sources (optical parametric oscillator, Brillouin laser) have become accessible and will be presented.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.418

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 head0.018
GPT teacher head0.209
Teacher spread0.191 · 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 designNot applicable
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
Published2023
Admission routes2
Has abstractyes

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