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Record W4394290904 · doi:10.6084/m9.figshare.20044283

Design and implementation of a Quarter Mode Substrate Integrated Waveguide (QMSIW) cavity filter

2022· dataset· en· W4394290904 on OpenAlexaboutno aff
Assal Alkhoder, Fariz Abboud

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Substrate (aquarium)Filter (signal processing)Mode (computer interface)WaveguideWaveguide filterMaterials scienceOptoelectronicsElectronic engineeringComputer scienceOpticsElectrical engineeringFilter designEngineeringPhysicsGeologyPrototype filterGeographyHuman–computer interaction

Abstract

fetched live from OpenAlex

AbstractIn this paper, a novel Quarter Mode Substrate Integrated Waveguide (QMSIW) cavity filter is presented. A prototype at 5GHz for the proposed filter has been simulated using CST Microwave Studio, and fabricated using standard Printed Circuit Board (PCB) process. The fabricated filter has been measured using a Vector Network Analyzer (VNA). The measurement results are compared with the simulation results, good agreement is observed between simulation and measurement results concerning the pass band, the selectivity and the out-of-band rejection.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.003

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 head0.024
GPT teacher head0.257
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 designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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