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Record W4367359423 · doi:10.1109/mias.2023.3261108

Taking Medium-Voltage Motor Control Centers to New Levels of Safety: A Quick and Simple Approach to Creating A Secure Work Environment

2023· article· en· W4367359423 on OpenAlexaff
John Kay, Navinchandra Bhatt, Jeffrey L. Fowler, David C. Mazur

Bibliographic record

VenueIEEE Industry Applications Magazine · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsRockwell Automation (Canada)
Fundersnot available
KeywordsPetrochemicalControl (management)Work (physics)Key (lock)Product (mathematics)Simple (philosophy)EngineeringComputer scienceManufacturing engineeringComputer securityEmbedded systemMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Medium-Voltage (MV) motor control centers designed around the requirements of UL 347[1]have been successfully utilized in petrochemical industries for many decades. Since they continue to provide consistent control of large MV assets, they have also become key components within the intelligent control requirements of today’s complex petrochemical facilities. Through the incorporation of proven technologies employed in other safety-based products used globally, these control products can be moved beyond their traditional safety requirements specified by the current product design and testing standards. This article covers the implementation and utilization of these proven technologies that can provide additional levels of personnel safety within these traditional designs.

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: Other · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.004

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 head0.073
GPT teacher head0.335
Teacher spread0.263 · 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
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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