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Record W4205358611 · doi:10.1115/qnde2021-74739

Coplanar Capacitive Sensing as a New Electromagnetic Technique for Non-Destructive Evaluation

2021· article· en· W4205358611 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversity of TorontoUniversité Laval
Fundersnot available
KeywordsCapacitive sensingMaterials scienceFinite element methodElectrodeAluminiumAcousticsCoplanar waveguideNondestructive testingElectric fieldComposite materialStructural engineeringElectrical engineeringComputer scienceEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract Coplanar capacitive technique is a relatively novel electro-magnetic Non-Destructive Testing (NDT) method that could be applied to the evaluation of materials by moving a set of electrodes on the surface of the specimen. In addition to the design-related parameters such as electrode shape, size, and the separation distance between the main electrodes, the material of the specimen affects the coplanar capacitive probe performance. In this paper, a 3D Finite Element Modeling (FEM) was employed to assess and identify the electric field behaviour as a function of material under test for non-conducting and conducting specimens with/without defect. Physical experiments were carried out by a pair of rectangular coplanar electrodes on an aluminium specimen with surface defects covered by a 5 mm thick plexiglass insulation layer to verify the simulation results and evaluate the performance of the probe. A good qualitative agreement was observed between the numerical simulations and experimental results.

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)0.000
metaresearch head score (Gemma)0.001
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: Methods
Teacher disagreement score0.384
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.017
GPT teacher head0.269
Teacher spread0.252 · 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

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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