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Record W2071355359 · doi:10.1520/jte101676

Simulation of Four-Point Test for DC Electrical Resistivity of Moderately Conductive Solids—Error due to Nonideal Specimen Size and Current Electrode Configuration

2008· article· en· W2071355359 on OpenAlexaff
David E. Woolley

Bibliographic record

VenueJournal of Testing and Evaluation · 2008
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsSaint-Gobain (Canada)
Fundersnot available
KeywordsElectrical resistivity and conductivityElectrical conductorElectrodeMaterials scienceCurrent (fluid)Point (geometry)Electrical currentComposite materialElectrical engineeringEngineeringMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

Abstract Electrical resistivity of moderately conductive solids is determined according to ASTM D4496 [1] using the four-probe method. The ideal specimen is long, thin, and of uniform cross section and composition. The ideal configuration of current electrodes is uniformly covering the opposite ends of the specimen to create a current path in the axial direction through the specimen. The purpose of this study is to determine what errors may arise from using nonideal specimen size or current-electrode configuration. The dc four-point resistivity test is simulated using a finite element (FE) model, which yields the electric potential distribution in the specimen and the electric current. Specimens in the form of bars and cylinders are studied. Simulations show that the calculated resistivity will be false-high when the current electrode is a band around the outside of the specimen or strip on one side of the specimen and when the parameter (R or H)/((Lo-Li)/2) is greater than 0.5, where R is the radius of a cylindrical specimen, H is the height of a bar-shaped specimen, (Lo-Li)/2 is the distance between a current electrode and the nearest potential electrode. The magnitude of the error reported in this study is on the order of the error that can arise due to errors in measuring dimensions of the specimen.

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)0.001
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.803
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
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 head0.118
GPT teacher head0.349
Teacher spread0.231 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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