The Unified Profile for DoDAF/MODAF (UPDM) enabling systems of systems on many levels
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it