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3.1.1 Model Lifecycle Management for MBSE

2014· article· en· W1507022657 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 · 2014
Typearticle
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsScope (computer science)VendorSystems engineeringComputer scienceSystem lifecycleImplementationEngineering managementProduct life-cycle managementUnified Modeling LanguageKey (lock)Process managementSoftware engineeringApplication lifecycle managementSoftwareEngineeringBusinessComputer security

Abstract

fetched live from OpenAlex

Abstract Model Based Systems Engineering (MBSE) is an evolving practice in the early stages of adoption similar to the mechanical, electrical and software domains 20 to 30 years ago. Today there is increasing recognition of the potential MBSE brings to system life cycle processes with the increasing complexity of systems and the demands of the global marketplace. In order for the practice to realize this potential, system modeling and MBSE must be part of the larger model based engineering effort, and integrate with other engineering discipline models and modeling activities across the life cycle of a system. This is placing increasing demands on the need for Model Lifecycle Management (MLM) as an essential part of an MBSE infrastructure. This paper establishes the motivation for MLM, as well as laying the foundation for addressing challenges that lay ahead. The paper is focused on describing key concepts, requirements, current practices, and future directions of MLM, and setting the basis for more in depth overview of MLM solutions and vendor offering that are beyond the scope of this paper.

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: Methods · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.419

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 head0.026
GPT teacher head0.268
Teacher spread0.242 · 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