Prioritization of Best Practices in the Implementation of Model‐Based Systems Engineering
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 Model‐based systems engineering (MBSE) is a form of systems engineering that is reported to lead to many benefits; yet few companies have successfully implemented MBSE, partly due to a predominance of guidance for adoption that does not recognize differences in organizational context. This paper uses a combination of a literature review and semi‐structured interviews to evaluate the hypothesis that the applicability of best practices varies on a case‐by‐case basis due to factors such as company size, industry, and location. A list of best practices is compiled from existing works and validated with interviews, ultimately resulting in a more complete and accurate list of best practices, along with a recommendation that each company should uniquely assess their circumstances and prioritize this list of best practices accordingly. The findings presented in this paper provide improved guidance towards the use of best practices for successful MBSE implementation.
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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) | 0.000 | 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