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Record W2164172264 · doi:10.1109/tia.2015.2468685

Analysis of Grounding Systems in the Vicinity of Hemispheroidal Heterogeneities

2015· article· en· W2164172264 on OpenAlexaff
Amir Hajiaboli, Simon Fortin, F. Dawalibi, Peter Zhao, Adrian Ngoly

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsMcGill UniversitySafe Engineering Services & Technologies (Canada)
Fundersnot available
KeywordsComputationOblate spheroidGroundMoment (physics)Poisson distributionPoisson's equationEarthing systemNumerical analysisMechanicsGeologyPhysicsComputer scienceMathematical analysisEngineeringClassical mechanicsMathematicsAlgorithmElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a method of analysis of grounding systems located inside or near a hemi-oblate spheroidal soil heterogeneity. This type of soil is particularly useful for modeling grounding grids close to certain types of finite inhomogeneities, such as lakes or some types of backfill materials. The developed analytical framework is based on a moment method and involves solving Poisson's equation in an oblate spheroidal coordinate system. Computation results obtained using this modeling approach for several electrodes and hemispheroidal geometries are compared with those obtained using other numerical and analytical techniques. In all cases, good agreement has been achieved.

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)0.000
metaresearch head score (Gemma)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.281
Teacher spread0.250 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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