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Record W2991458603 · doi:10.1109/ipcon.2019.8908505

Recent Advances in Metamaterial Integrated Photonics

2019· article· en· W2991458603 on OpenAlexaff
Pavel Cheben, Jiřı́ Čtyroký, Daniele Melati, Yuri Grinberg, Alejandro Ortega‐Moñux, J. Gonzalo Wangüemert‐Pérez, Íñigo Molina‐Fernández, Aitor V. Velasco, Alaine Herrero-Bermello, J. Lapointe, Siegfried Janz, Jens H. Schmid, Dan‐Xia Xu, Ross Cheriton, Mohsen Kamandar Dezfouli, S. Wang, M. Vachon, Laurent Vivien, Winnie N. Ye, Ján Litvík, Milan Dado, Robert Halir, Carlos Alonso‐Ramos, Daniel Benedikovič, Alejandro Sánchez‐Postigo, José Manuel Luque‐González, David González‐Andrade, Daniel Pereira-Martín

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsCarleton UniversityNational Research Council Canada
Fundersnot available
KeywordsMetamaterialPhotonicsComputer scienceOptoelectronicsMaterials science

Abstract

fetched live from OpenAlex

Metamaterial engineered waveguide structures are emerging as fundamental building blocks for integrated photonics. Here we present an overview of our recent advances in this field, including fiber-chip couplers, ultra-broadband beam splitters, nanophotonic waveguides with engineered anisotropy and integrated Bragg filters.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.215
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2019
Admission routes1
Has abstractyes

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