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Record W2155870562 · doi:10.5006/c2010-10107

Measurements of Higher Harmonics in AC Interference on Pipelines

2010· article· en· W2155870562 on OpenAlexaff
D. H. Boteler, S.J. Croall, Peter Nicholson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsResearch ManitobaNatural Resources Canada
Fundersnot available
KeywordsHarmonicsInterference (communication)Pipeline transportMaterials scienceElectromagnetic interferenceHarmonic analysisElectrical engineeringElectronic engineeringAcousticsEngineeringPhysicsMechanical engineeringVoltageChannel (broadcasting)

Abstract

fetched live from OpenAlex

Abstract Recordings of alternating current (AC) pipe to soil potential (PSP) variations have been made using fast-sample (2000 samples/sec) dataloggers. This sampling rate is fast enough to show the waveform of the AC potential variations. The recordings were made on two sections of a pipeline sharing a right-of-way with an AC transmission line. Global Positioning System (GPS) timing on the dataloggers allows the PSP variations to be recorded accurately enough to determine the phase relation between the AC potential variations at different places along the pipeline. A notable feature of the recordings is that they show significant PSP variations, not only at the fundamental AC frequency (60 Hz) but also at the third harmonic (180 Hz). This prompted a comparison of the factors affecting the size of the fundamental and third harmonic PSP variations in a typical pipeline. It is shown that the pipeline can be many hundreds of times more sensitive to the third harmonic than to the fundamental. This suggests that more attention should be paid to the size of the third harmonic AC currents in power lines adjacent to pipelines.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.289
Teacher spread0.212 · 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 designObservational
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

Citations7
Published2010
Admission routes1
Has abstractyes

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