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Record W2902343812 · doi:10.1109/apeie.2018.8546309

Losses in a Superconducting Quarter Wavelength Coplanar Resonator

2018· article· en· W2902343812 on OpenAlexaboutno aff
Evgeniya Mutsenik, B. I. Ivanov, Dmitriy K. Pitsun, Aidar N. Sultanov, Oleg V. Sevostyanov, А. Г. Вострецов

Bibliographic record

Venue2018 XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE) · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsResonatorWavelengthSuperconductivityQuarter (Canadian coin)Dispersion (optics)Coplanar waveguideMaterials scienceOptoelectronicsOpticsPhysicsElectronic engineeringEngineeringCondensed matter physics

Abstract

fetched live from OpenAlex

In this paper we present results of the experimental investigation of quarter wavelength coplanar resonator at millikelvin temperatures. Also, a comparison with the data obtained from analytical expressions and from electromagnetic simulations is made. It is shown that the previously developed design methodology of such resonators is in a good agreement with real parameters of the system. It is experimentally shown that in the manufactured sample there are parasitic two-level defects, in fact objects of the quantum nature which presence explains small dispersion of the expected parameters. Their influence on the main characteristics of the system was studied.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.028
GPT teacher head0.261
Teacher spread0.233 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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