MétaCan
Menu
Back to cohort
Record W2107289035 · doi:10.1109/tmtt.2003.810139

Modified transmission-reflection method for measuring constitutive parameters of thin flexible high-loss materials

2003· article· en· W2107289035 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2003
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsDefence Research and Development CanadaCanadian Armed ForcesUniversity of VictoriaUniversity of Calgary
Fundersnot available
KeywordsMaterials scienceDielectricReflection (computer programming)PermittivityScattering parametersLoss factorDielectric lossOpticsTransmission lossElectronic engineeringOptoelectronicsComputer sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

The transmission-reflection method is modified for measuring constitutive parameters of thin high-loss materials used as radar absorbers. The method uses a two-layer structure, consisting of a layer of thin flexible unknown material supported by a thicker rigid known material. The analysis and measurements focus on nonmagnetic samples of a high dielectric constant and loss factor and on the waveguide configuration in the X-band. A nonlinear least-squares optimization is used to obtain the complex permittivity from the measured scattering parameters. The uncertainty analysis presented facilitates selection of the support layer thickness. Simulations with the finite-difference time-domain method explore the effects of sample imperfections. Accuracy of a few percent can be achieved for a sample thickness of a fraction of a millimeter, provided that the thickness of the support dielectric is close to optimum and sample has only small surface imperfections.

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.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.794
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.031
GPT teacher head0.269
Teacher spread0.238 · 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