A data‐centric capability‐focused approach for system‐of‐systems architecture modeling and analysis
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
Abstract A data‐centric, capability‐focused approach is proposed to facilitate the architecture modeling and analysis of challenging system‐of‐systems (SoS). This approach abstracts essential information from the underlying complexity with the architecture modeling in a data‐centric and semantically consistent fashion, and allows early understanding and exploration of the logical, behavioral, and performance characteristics to achieve the desired capabilities. More specifically, a high‐level data meta‐model, depicting the semantic relationships of constituent architectural data elements, is first presented to guide the architectural data modeling, which is aligned well with the US Department of Defense Architecture Framework (DoDAF) Meta‐model (DM2). Then, the development of architectural descriptions and the construction of executable models are studied based on the core architectural data elements. Additionally, architecture analysis using static and executable models are discussed, including static analysis, dynamic analysis, and experimental analysis. The feasibility of the foregoing approach is demonstrated with an illustrative example. ©2013 Wiley Periodicals, Inc. Syst Eng 16:
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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