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Record W2098815925 · doi:10.1061/9780784479414.015

Best Practices for the Design of 115KV to 230KV Overhead Transmission Lines

2015· article· en· W2098815925 on OpenAlexaff
Leon Kempner, Asim Haldar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsCégep de Sherbrooke
Fundersnot available
KeywordsReliability (semiconductor)Electric power transmissionOverhead (engineering)Computer scienceQuality (philosophy)Reliability engineeringTransmission (telecommunications)Balance (ability)Routing (electronic design automation)Transmission lineArchitectural engineeringEngineeringRisk analysis (engineering)TelecommunicationsElectrical engineeringBusinessComputer network

Abstract

fetched live from OpenAlex

Electrical utilities are interested in building well engineered new and replacement transmission lines that balance the many elements of their design. The public is concerned about impacts on land use and how the aesthetics of the line will impact their quality of life and the value of their property. The utilities and the public are both concerned about reliability, and both are also concerned about the cost effectiveness of the balance between the competing forces of aesthetics, reliability, and economy. A guide was developed with the objective of providing a comprehensive, single, document that discusses the elements of 115kV to 230kV transmission lines, including materials, configurations, insulation, reliability, EMF, and economics. The guide also includes some other (non-design-related) aspects of transmission lines, such as routing, permitting, and public acceptance. This paper summarizes information presented in the guide. The topic of this paper will be of interest to both US and international utilities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.207

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)0.0000.000

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 head0.082
GPT teacher head0.316
Teacher spread0.234 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2015
Admission routes1
Has abstractyes

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