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Record W2020316800 · doi:10.1109/mias.2009.934966

The best concepts from the NEC, CEC, and IEC

2009· article· en· W2020316800 on OpenAlexaffabout
Marty Cole, Tim Driscoll, Rob Roberton, George Morlidge, K.E. Martin

Bibliographic record

VenueIEEE Industry Applications Magazine · 2009
Typearticle
Languageen
FieldEngineering
TopicElectrical Fault Detection and Protection
Canadian institutionsShell (Canada)Bell (Canada)
Fundersnot available
KeywordsFlexibility (engineering)Best practiceJurisdictionEngineeringSystems engineeringComputer scienceManufacturing engineeringReliability engineeringManagementPolitical science

Abstract

fetched live from OpenAlex

In Alberta, Canada, the authority having jurisdiction, or the regulator, has allowed qualified users the flexibility to adapt or combine elements from the CEC, NEC, and IEC into a single operating system to take advantage of the best in global technologies. This has resulted in a unique blended system that combines the best of the traditional North American and international approaches and provides cost savings over traditional electrical installations. This article discusses some of the elements used to develop this hybrid approach in terms of global technology equipment designs and wiring practices.

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)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0060.004
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.005

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 head0.012
GPT teacher head0.262
Teacher spread0.251 · 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
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

Citations4
Published2009
Admission routes2
Has abstractyes

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