MétaCan
Menu
Back to cohort
Record W4388109954 · doi:10.1364/ome.510620

Reconfigurable photonic platforms: feature issue introduction

2023· article· en· W4388109954 on OpenAlexaff
Behrad Gholipour, Nathan Youngblood, Qian Wang, Pin Chieh Wu, Paul E. Barclay, Jun‐Yu Ou

Bibliographic record

VenueOptical Materials Express · 2023
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersEngineering and Physical Sciences Research Council
KeywordsPhotonicsFeature (linguistics)Materials scienceNanotechnologyPhotonic crystalGlobeComputer scienceOptical materialsOptoelectronicsEngineering physicsEngineering

Abstract

fetched live from OpenAlex

We introduce the feature issue on Reconfigurable Photonic Platforms. This issue presents a broad collection of contributions from across the globe, bringing together different sub-topics on the fundamentals, new research trends, and applications of volatile and non-volatile platforms utilizing oxides and nitrides, liquid crystals, chalcogenides as well as magneto-optical and ferroelectric material platforms.

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: Editorial · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.004

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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
GenreEditorial

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueOptical Materials ExpressSame topicNeural Networks and Reservoir ComputingFrench-language works237,207