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Record W1985282888 · doi:10.1145/1501434.1501512

Towards an MDA-oriented UML profile for critical infrastructure modeling

2006· article· en· W1985282888 on OpenAlexaff
Ebrahim Bagheri, Ali A. Ghorbani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMetamodelingUnified Modeling LanguageComputer scienceInterdependenceComponent (thermodynamics)Applications of UMLSoftware engineeringSequence diagramSystems engineeringProgramming languageEngineeringSoftware

Abstract

fetched live from OpenAlex

Infrastructures are networks of highly complex systems that can be classified as socio-technical organisms with hidden consciousness. The hidden consciousness of these types of systems lies beyond their definition. Although these systems are structurally independent of any outside component, but collaborate synergistically to provide their services to the end customer. The interdependencies between these complex systems bring about sophisticated and unpredictable outcomes. In this paper we propose a platform independent metamodel for critical infrastructures. The metamodel precisely defines every aspect of an infrastructure through clear syntactical and semantic definition of existing concepts and relationships. The Platform independent model (PIM) has been defined as a UML profile (UML-CI) and serves as one of the first steps towards building an agent based simulation environment.

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)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0060.005
Open science0.0030.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.002

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 head0.085
GPT teacher head0.448
Teacher spread0.362 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations9
Published2006
Admission routes1
Has abstractyes

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