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Record W4402651630 · doi:10.1051/e3sconf/202456904003

Study pertaining to leak simulations in electrical leak location dipole testing. A better understanding of dipole testing calibration using various plate sizes and shapes

2024· article· en· W4402651630 on OpenAlexaff
Francis Labonté, C. Charpentier

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsLeakCalibrationDipoleLeak detectionReliability engineeringMaterials scienceComputer sciencePhysicsEngineeringMathematicsStatisticsThermodynamics

Abstract

fetched live from OpenAlex

According to standard ASTM D7007 for geoelectrical leak location (ELL) surveys on earth materials, a hole simulation or calibration must be performed before starting the actual survey. The standard also states that a 1/4 in diameter metal plate must be detectable based on specific signal strengths, if not, the survey cannot be considered completed by the ASTM standard and must be conducted on a 1 m x 1 m grid. Most ELL companies only perform the 1/4 inch test to see if the surveying speed will be fast or slow. The better the signal, the larger the grid, the faster the survey. Usually, leak location practitioners will then proceed to the leak location either way. The number of projects that fail the 1/4 inch test is quite important, so how do we push things further to ensure the best efficiency on site? What information is necessary to understand how the site responds to the test and what can we do to improve the quality of the survey? Here we will compare data gathered within 11 years with various calibration plate sizes and will explain the effects of field specific parameters, such as the simulation’s position, moisture, thickness, cover material homogeneity, and the most important factor of all: peripherical electrical isolation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.066
GPT teacher head0.291
Teacher spread0.224 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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