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Record W2589017685 · doi:10.1109/mnano.2016.2635178

Understanding the Underlying Mechanisms [The Editor's Desk]

2017· article· en· W2589017685 on OpenAlexaboutno aff
John T. W. Yeow

Bibliographic record

VenueIEEE Nanotechnology Magazine · 2017
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsnot available
Fundersnot available
KeywordsPhotonicsDeskFabricationCharacterization (materials science)NanotechnologyComputer scienceElectronicsCascadeMeasure (data warehouse)Materials scienceOptoelectronicsEngineering physicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

To optimize the functioning of electronics and photonic devices, we need to understand the underlying mechanisms that govern their behaviors during operation. We already have access to a variety of techniques for characterizing device performance during equilibrium; however, we have limited options for making measurements during device operation. A recent development in scanning voltage microscopy (SVM) has enabled the characterization of the internal behavior of operating devices. The work at the Khalifa University of Science and Research in the United Arab Emirates and the University of Waterloo in Ontario, Canada, is focused on using SVM to measure the electrical properties of quantum cascade and interband cascade lasers. The first article, "Scanning Voltage Microscopy for Emerging Electronic and Photonic Devices" by Mahmud et al., discusses the fabrication process of the nanotips and the effects of the nanotip shape on its measurement ability.

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)0.002
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0040.001
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0260.022

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 head0.044
GPT teacher head0.249
Teacher spread0.205 · 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
GenreCommentary

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
Published2017
Admission routes1
Has abstractyes

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