1.3.2 A Vision for Super‐Model Driven Systems Engineering
Bibliographic record
Abstract
Abstract Model‐Based Systems Engineering (MBSE) has been developing for some time, and has recently acquired new impetus with the completion of the Systems Modeling Language (SysML). This paper envisions taking MBSE much further, to a future of highly integrated and automated design and verification coupled with advances in simulation and domain linkage to allow the synthesis of complete systems from requirements into mathematical models and then into physical realizations. This would permit the application of three of the most successful approaches from agile software development, namely rapid, iterative development of the system starting with the highest value functions, facilitating continual reassessment of the future direction, and continual regression testing to ensure that system bugs are identified and removed rapidly. We envisage the requirements and the model evolving together from proto‐requirements and proto‐model in increasing detail until the point at which the model can be realized with real hardware and software. Taking this further, the MBSE engine can perform trade‐offs and optimization on the design. Implementing this vision requires progress in a number of technologies, such as data exchange between domain tools. At this time, much engineering effort is consumed in people communicating and mediating information and translating it from one form to another (e.g. system design to mechanical design). If we can realize the vision proposed, we can remove much of the burden of information mediation and optimization, allowing engineers to focus on their expertise and larger issues. The potential savings in labour are huge.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".