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Record W2110954663 · doi:10.1109/mwsym.1989.38959

Analysis and tuning efficiency optimization of magnetically tuned printed E-plane circuit filters

2003· article· en· W2110954663 on OpenAlexaff
Jason Uher, W.J.R. Hoefer

Bibliographic record

VenueIEEE MTT-S International Microwave Symposium digest · 2003
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFerrite (magnet)SlabMaterials scienceInsertion lossMagnetWaveguide filterScattering parametersWaveguideFilter (signal processing)OpticsSaturation (graph theory)Electronic engineeringOptoelectronicsPrototype filterAcousticsLow-pass filterPhysicsComposite materialElectrical engineeringEngineeringMathematicsStructural engineering

Abstract

fetched live from OpenAlex

The tuning efficiency of a metal insert filter loaded with ferrite slabs is defined and analyzed. Three parameters which dominate the filter tunability (ferrite slab thickness, distance to the narrow waveguide wall, and ferrite saturation magnetization) are discussed. An optimum combination of these parameters has been found, and a novel filter design based on the model scattering method is presented. The improved filters can be tuned over 60-70% of a standard waveguide band with an insertion loss between 1 and 3 dB, depending on frequency.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.573
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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 teacher head, not a consensus.

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

Citations2
Published2003
Admission routes1
Has abstractyes

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