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Record W4391827248 · doi:10.1109/mie.2023.3321390

Synergies of Operation, Information, and Communication Technology for Solving New Societal and Industrial Challenges: Future Directions

2024· article· en· W4391827248 on OpenAlexaff
Wenbin Dai, Paulo Leitão, Kim Fung Tsang, Yang Shi, Gerhard P. Hancke, Moris Behnam, Jan Haase, Valeriy Vyatkin

Bibliographic record

VenueIEEE Industrial Electronics Magazine · 2024
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsVirtualizationAutomationSustainabilityInformation and Communications TechnologyInformation technologyComputer scienceBusinessEngineeringEngineering managementRisk analysis (engineering)Cloud computing

Abstract

fetched live from OpenAlex

The world is facing a series of new societal and industrial challenges, such as continuously increasing costs in food and energy supply and a short supply of skilled labor. To solve these new challenges, operation, information, and communication technology is entering a new era with more focus targeted to cost reduction and energy savings. In this article, how innovative technologies including virtualization, low-code development, digital twins, and industrial agents can support and impact digital and green transitions is analyzed. In addition, how these technologies will help the security and sustainability of future industrial automation systems is also discussed.

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: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.547

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.235
Teacher spread0.216 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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