MétaCan
Menu
Back to cohort
Record W2101585606 · doi:10.1109/tmag.2005.852950

High-frequency giant magnetoimpedance measurement and complex permeability behavior of soft magnetic co-based ribbons

2005· article· en· W2101585606 on OpenAlexafffund
M. Heshmatzadeh, Xuezhi Zhou, Greg E. Bridges

Bibliographic record

VenueIEEE Transactions on Magnetics · 2005
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaNanjing UniversityUniversity of ManitobaNanjing Normal University
KeywordsRibbonSkin effectMaterials scienceConductorGiant magnetoimpedancePermeability (electromagnetism)Magnetic fieldElectrical impedanceRelative permeabilityNuclear magnetic resonanceMagnetostrictionCharacteristic impedanceTransmission lineCondensed matter physicsInductanceComposite materialPhysicsGiant magnetoresistanceMagnetoresistanceElectrical engineeringMembranePorosityChemistry

Abstract

fetched live from OpenAlex

We present a method for extracting the magnetic parameters of a soft magnetic Co-based alloy (Co/sub 68.25/Fe/sub 4.5/Si/sub 12.25/B/sub 15/) in the high-frequency regime (100 MHz-2.5 GHz). The method uses the magnetic sample (as-cast ribbon) as the conductor of a terminated microstrip transmission line. It uses the complex propagation constant of the line, obtained from open/short circuit input impedance measurements, to extract the per-unit-length series impedance. It then uses a theoretical model, based on the distributed parameters and the skin effect, to extract the bulk transverse relative permeability. The model considers the geometrical configuration of the magnetic conductor (ribbon) when determining the series impedance as well as the complex transverse relative permeability (/spl mu//sub tr/=/spl mu/'/sub tr/-j /spl mu/''/sub tr/). We report on the effect of applying and varying an external dc magnetic field.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.230
Teacher spread0.203 · 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

Citations8
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on MagneticsSame topicMetallic Glasses and Amorphous AlloysFrench-language works237,207