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Record W2426451253 · doi:10.5006/c2016-07393

AC Interference Risk Ranking: Case Study

2016· article· en· W2426451253 on OpenAlexaff
Wolfgang Fieltsch, Daniel Silva

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical Fault Detection and Protection
Canadian institutionsStantec (Canada)Cochrane
Fundersnot available
KeywordsInterference (communication)Ranking (information retrieval)Electromagnetic interferenceComputer scienceMaterials scienceArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

Abstract As utility corridors become increasingly congested, AC interference on pipelines due to collocation with high voltage AC (HVAC) transmission powerlines continues to be a growing concern. Many pipeline operators have large quantities of existing pipeline infrastructure that has not been fully assessed to determine whether it is at risk due to AC interference. The primary risks on these pipelines under powerline steady-state conditions are safety and AC corrosion. This paper is a case study of a project involving AC interference risk ranking of over 6,400 miles (10,300 km) of existing transmission piping operated by one of the largest combination gas and electric utilities in the United States. The scope of this project is to identify the transmission pipelines that are at greatest risk due to steady-state AC interference, to prioritize them based on the severity of risk, and to determine what further action is required. Once the ranking is completed, it is envisioned that AC interference studies, and the design and implementation of mitigation and monitoring systems will be performed on the at risk pipeline systems in order of priority as part of a multi-year program.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.238
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 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

Citations2
Published2016
Admission routes1
Has abstractyes

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