MétaCan
Menu
Back to cohort
Record W145137833 · doi:10.5006/c2000-00752

On the Mechanisms of Electromagnetic Interference between Electrical Power Systems and Neighboring Pipelines

2000· article· en· W145137833 on OpenAlexaff
F. Dawalibi, Y. Li, Robert Southey, Jianjun Ma

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsSafe Engineering Services & Technologies (Canada)
Fundersnot available
KeywordsElectromagnetic interferencePipeline transportInterference (communication)Electrical engineeringPower (physics)Materials scienceComputer scienceElectronic engineeringEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract The mechanisms of electromagnetic interference between a power line and a neighboring pipeline using electromagnetic field theory are discussed based on a simple right-of-way scenario. First, the field theory approach is used to model the complete conductor network under consideration, as is. The inductive, capacitive and conductive interference effects between all the elements in the network are simultaneously taken into account in one single step. The computed results are then used to develop new computer models whereby the effects of the inductive, capacitive and conductive interference effects can be separated. This allows us to compare the field-theory-based results with the results obtained from other approximated approaches, such as grounding analysis (conductive effects) and circuit-based models (inductive effects). The effects of a typical mitigation system on the interference levels are also studied. The results presented in this paper clearly illustrate the mechanisms of electromagnetic interference between electrical networks and neighboring metallic utilities. Furthermore, the methods used here should help develop more accurate approximations when modeling non parallel pipelines using conventional circuit theory.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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 designTheoretical or conceptual
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

Citations12
Published2000
Admission routes1
Has abstractyes

Explore more

Same topicLightning and Electromagnetic PhenomenaFrench-language works237,207