MétaCan
Menu
Back to cohort
Record W2139874158 · doi:10.1109/tpwrd.2012.2199523

Spatial Analysis of Thermal Aging of Overhead Transmission Conductors

2012· article· en· W2139874158 on OpenAlexaff
Petr Musı́lek, Jana Heckenbergerová, Md. Mafijul Islam Bhuiyan

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2012
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectrical conductorTransmission lineConductorElectric power transmissionOverhead lineOverhead (engineering)Transmission (telecommunications)Scheduling (production processes)ThermalPoint (geometry)Asset managementComputer scienceEngineeringElectrical engineeringMeteorologyMaterials scienceGeographyMathematics

Abstract

fetched live from OpenAlex

This paper introduces a new methodology for spatial analysis of conductor thermal aging that can be performed at three different levels: point, line, and area. The methodology uses known characteristics of transmission conductors, along with load and weather data, to determine time series of conductor temperatures and corresponding thermal aging. Weather conditions can be obtained with high resolution, providing environmental conditions virtually at every point of a transmission system. This novel approach provides a complete spatiotemporal view of the thermal state of the system, bringing a whole new dimension to the research of thermal aging. All described types of aging analysis are important for effective transmission asset management, for scheduling of line maintenance or inspections, and for planning future transmission systems.

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 score0.004

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)0.0000.000

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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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

Citations46
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Power DeliverySame topicThermal Analysis in Power TransmissionFrench-language works237,207