The Unified Profile for DoDAF/MODAF (UPDM) enabling systems of systems on many levels
Bibliographic record
Abstract
The Unified Profile for DoDAF and MODAF (UPDM) initiative was started by members of INCOSE and the OMG to create a standard profile for DoDAF and MODAF. Although the main goal was to create a standard UML profile for DoDAF and MODAF, UPDM fulfills other goals as well. The standardized format decreases training requirements as well as providing a standard display format, thus improving communication. UPDM also includes concepts found in the recently created Systems Modeling Language (SysML) providing flow-down and traceability to systems development. SysML parametric diagrams provide trade-off analysis via quantitative analysis with equation solvers and simulation tools. SysML also provides requirements traceability with its requirements model and allocation across levels of abstraction and separation of concerns. The US DoD and UK MOD are interested in leveraging commercial standards for their Military Architecture Frameworks and the UPDM standard meets this goal as it is an OMG standard and will be considered for ISO standardization. Interoperability between Military Architecture Framework Tools is provided via OMG XMI. The common meta-model also provides interoperability between MODAF and DoDAF frameworks. Additional frameworks are already planned to be added such as the NATO framework NAF and the Canadian DNDAF. Additional features such as Human Factors, architectural patterns, and information assurance can be more easily integrated. Finally, the number of tool vendors implementing this standard means improved tools, increased competition, and additional choice for system architect. This paper looks at UPDM, how it will improve the state of the art for system architects, and enable interchange of architectural information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.025 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".