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Record W2611261729 · doi:10.5006/c2017-09455

The AC Close Interval Survey and Other Common AC Measurement Errors

2017· article· en· W2611261729 on OpenAlexaff
Wolfgang Fieltsch, Robert G. Wakelin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsPhilips (Canada)Stantec (Canada)Cochrane
Fundersnot available
KeywordsInterval (graph theory)Materials scienceElectrical engineeringPhysicsEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract The measurement of induced AC voltages along a pipeline is a primary indicator of electrical safety hazards and AC corrosion risks under steady state operation of influencing powerlines. This paper addresses several fallacies, misconceptions and common errors related to the measurement of these AC induced voltages. Many operators monitor AC voltage levels at test stations on an annual basis as part of their cathodic protection survey. However, the locations of the test stations and pipeline AC voltage peaks do not always coincide. In an attempt to determine the AC voltage profile along the pipeline, some operators and consultants perform AC close interval surveys. Through the combined use of basic electrical principles, computer modeling, and an assessment of close interval survey data, this technique is shown to be invalid. Another characteristic of AC interference on pipelines that is often overlooked is the variability of the measured AC voltage, which will fluctuate with the powerline loading throughout the day, from day to day, seasonally and annually. A one-time, annual measurement at test stations is not a reliable indicator of the AC interference risk to the pipeline. Finally, induced AC voltages should be measured with respect to remote earth. When an AC voltage is in proximity to a grounding electrode or some other AC mitigation facility, the AC current discharging to the ground creates a potential gradient in the soil, which will result in the measured voltage being less than the actual voltage to remote earth.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.454
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.054
GPT teacher head0.279
Teacher spread0.225 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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