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Record W1540107160 · doi:10.1109/pes.2004.1372965

An intelligent substation design environment

2004· article· en· W1540107160 on OpenAlexaff
D. Atanackovic, D. McGillis, F.D. Galiana

Bibliographic record

VenueIEEE Power Engineering Society General Meeting, 2004. · 2004
Typearticle
Languageen
FieldEngineering
TopicPower Systems and Technologies
Canadian institutionsMcGill UniversityHydro-QuébecBC Hydro (Canada)
Fundersnot available
KeywordsReliability engineeringReliability (semiconductor)Computer sciencePrincipal (computer security)VoltageLine (geometry)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Summary form only given. A knowledge-based object-oriented tool has been developed to integrate, preserve and support tasks inherent to substation design, bringing together the principal design activities and related data. It produces a number of potential designs which satisfy specified design criteria such as short-circuit levels, insulation withstand voltages and substation reliability. The outputs of the tool include: (1) the definition of the main substation equipment; (2) their general electric specifications; (3) the single-line diagram of the station; (4) a detailed report describing the design performance including reliability measures and cost.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.100
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1000.032

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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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 designNot applicable
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

Citations1
Published2004
Admission routes1
Has abstractyes

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