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Record W2155322898 · doi:10.1109/61.915489

Software models for relays

2001· article· en· W2155322898 on OpenAlexaff
P.G. McLaren, K.K. Mustaphi, G. Benmouyal, S. Chano, A.A. Girgis, Charles Henville, Mladen Kezunović, L. Kojovic, R.J. Marttila, Mike Meisinger, Guillaume Michel, Manoj Sachdev, V. Skendžić, T.S. Sidhu, D.A. Tziouvaras

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2001
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsParkinson Canada
Fundersnot available
KeywordsComputer scienceSoftwareRelayProcess (computing)Protective relaySystems engineeringReliability engineeringSoftware engineeringEngineeringPower (physics)

Abstract

fetched live from OpenAlex

This paper reviews the past and present uses of relay models. It discusses the various types of models, what information is required to build such models and the model validation process. Examples of present and possible future use of software models are given.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.005

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 head0.016
GPT teacher head0.213
Teacher spread0.197 · 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 designSimulation or modeling
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

Citations76
Published2001
Admission routes1
Has abstractyes

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