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Record W3091412513 · doi:10.1002/adpr.202000026

Ultracompact Lens‐Less “Spectrometer in Fiber” Based on Chirped Filament‐Array Gratings

2020· article· en· W3091412513 on OpenAlexafffund
Abdullah Rahnama, Keivan Mahmoud Aghdami, Young Hwan Kim, Peter R. Herman

Bibliographic record

VenueAdvanced Photonics Research · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsOpticsMaterials scienceCladding (metalworking)Fiber Bragg gratingFemtosecondSpectrometerOptical fiberPHOSFOSLong-period fiber gratingChirpGratingPhotonic-crystal fiberOptoelectronicsLaserLens (geology)Graded-index fiberFiber optic sensorPhysics

Abstract

fetched live from OpenAlex

Femtosecond laser irradiation is applied to a single‐mode optical fiber to embed a filament array through the silica cladding and guiding core and form chirped Bragg gratings. Unlike a planar‐shaped refractive index modification, the long and uniform filament facilitates efficient optical scattering into azimuthally narrowed radiation modes, external and transverse to the fiber cladding. Chirping of the grating period further provides spectral focusing. The combined spectral and azimuthal focusing permits lens‐less recording of bright and high‐resolution spectra spanning across most of the visible band with a low‐cost charged coupled device camera. The flexible point‐by‐point writing enables fiber tapping of light with engineered spectral and geometric focusing properties, permitting the design of new compact photonic devices based on the all‐fiber spectrometer.

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 score0.004

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)0.0010.000

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 head0.060
GPT teacher head0.327
Teacher spread0.266 · 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

Citations27
Published2020
Admission routes2
Has abstractyes

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