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Record W3124127659 · doi:10.1109/mic.2021.3051902

The Forging of Autonomic and Cooperating Digital Twins

2021· article· en· W3124127659 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Internet Computing · 2021
Typearticle
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsUniversity of Victoria
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaInternational Business Machines Corporation
KeywordsAutonomyComputer scienceConvergence (economics)Realization (probability)ForgingMetaverseVirtual realityKnowledge managementHuman–computer interactionEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Digital twins (DTs) will enable the long-anticipated convergence between physical and virtual worlds. This disruptive convergence will augment operations and services that are traditionally constrained to physical spaces with new virtual-based capabilities. Nevertheless, achieving this point to its full extent will demand DTs with increased autonomy and enhanced ability to monitor, reason about, and react upon relevant phenomena. This article discusses pivotal research advances toward the realization of autonomic and cooperative DTs. We elaborate on fundamental technical considerations of advanced and robust DTs by describing a reference framework that enables increased autonomy and enhanced cooperation. Then, we identify opportunities for further DT research and technological advances in diverse contexts.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.307

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 head0.011
GPT teacher head0.214
Teacher spread0.202 · 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