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Record W2121590093 · doi:10.1109/te.2002.1024617

Teaching transmission lines: a project of measurement and simulation

2002· article· en· W2121590093 on OpenAlexaff
Bruce G. Colpitts

Bibliographic record

VenueIEEE Transactions on Education · 2002
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsTransmission lineElectric power transmissionAttenuationElectrical impedanceElectronic engineeringComputer scienceCharacteristic impedanceCrosstalkNetwork analyzer (electrical)AcousticsElectrical engineeringEngineeringTelecommunicationsOpticsPhysics

Abstract

fetched live from OpenAlex

A junior-year project for a course in electromagnetic waves is described, including the theory, hardware, and basic measurements. The essence of the project is to simulate transmission-line properties based upon theory developed in the classroom and to measure those properties in the laboratory for comparison. The equipment chosen is readily available and inexpensive, but is used here to illustrate concepts usually requiring an expensive vector network analyzer. The transmission-line properties of insertion loss, input impedance, and crosstalk are measured as a function of frequency on Category 5 cable. The transmission-line phase shift, propagation velocity, attenuation, characteristic impedance, impedance under various transmission-line configurations, and crosstalk are modeled and measured. Measured and theoretical results are in good agreement, reinforcing the strength of the underlying theory for the student. Evaluation of the project over a three-year period with more than 120 students is very positive in terms of developing confidence in and understanding of this abstract material.

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)0.008
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.046
GPT teacher head0.285
Teacher spread0.239 · 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 designSimulation or modeling
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

Citations10
Published2002
Admission routes1
Has abstractyes

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