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9.2.1 Using SysML and UML To Develop and Implement Interoperable System Components for Engagement Simulations

2008· article· en· W2050057498 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.

Bibliographic record

VenueINCOSE International Symposium · 2008
Typearticle
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsSystems Modeling LanguageUnified Modeling LanguageInteroperabilityComputer scienceSoftware engineeringSystems engineeringApplications of UMLEngineeringProgramming languageWorld Wide WebSoftware

Abstract

fetched live from OpenAlex

Abstract The Torpedo Enterprise Advanced Modeling and Simulation (TEAMS) project is an initiative funded by the Office of Naval Research to develop a cross‐enterprise, collaborative undersea warfare modeling and simulation (M&S) environment using reusable components that can be composed into simulations. This environment will include an open systems architecture and result in the sharing and leveraging of legacy and new‐development resources; it will support the development of M&S tools and the application of these tools across the lifecycle of undersea weapons. TEAMS initial model was delivered in the Unified Modeling Language (UML). When the Object Management Group (OMG) adopted the OMG System Modeling Language (OMG SysML™) specification, the Open Systems Joint Task Force of the Office of Secretary of Defense funded TEAMS to assess the utility of applying SysML in place of UML. This paper describes the torpedo M&S domain as it relates to TEAMS, discusses the application of SysML to existing UML artifacts, and assesses the utility of SysML from the TEAMS perspective.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.537

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 head0.135
GPT teacher head0.329
Teacher spread0.193 · 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