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Record W2325956562 · doi:10.1061/40798(190)62

A Process for C2P Harmonization: Electrical Standards in North America

2006· article· en· W2325956562 on OpenAlexaffabout
Brian Haydon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems and Technologies
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsScope (computer science)HarmonizationWork (physics)Process (computing)Critical infrastructureJurisdictionBest practiceLegislationRisk analysis (engineering)BusinessComputer scienceComputer securityEngineeringPolitical scienceLaw

Abstract

fetched live from OpenAlex

Recent events have shown that natural and human made disasters, acts of terrorism and massive technological failures can have a profound impact on the safety and security of people and critical infrastructure. Given the scope of existing codes and standards, the development work sponsored by the ASCE/AEI, "Recommended Practices for Control, Communication and Power (C2P) of Critical Facilities", to identify ways to improve the resistance of critical electrical infrastructure to disaster, is essential. These recommended practices are intended to augment and not replace engineering requirements promulgated or enforced by the regulatory bodies and authorities having jurisdiction. This work presents a compelling opportunity to embrace a harmonized solution — a single document that will draw on the experiences and lessons of the standards community throughout North America to address issues that are mutually imperative. A harmonized approach is preferred over a national one for several reasons, as this paper will explore. The interests of manufacturers, regulators, specifiers, and users will best be served if Canadian and U.S. representatives undertake the C2P work jointly, with a harmonized document as its outcome. The experience of the standards community in harmonization efforts provides important insights that may be applied to a harmonized C2P process.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.693
Threshold uncertainty score0.221

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 head0.007
GPT teacher head0.224
Teacher spread0.218 · 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 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

Citations0
Published2006
Admission routes2
Has abstractyes

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