3.4.0 Integrating MBSE into a Multi‐Disciplinary Engineering Environment
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) formalizes the practice of systems engineering through the use of models. This panel is intended to address considerations for incorporating MBSE into a broader multi‐disciplinary engineering environment. Engineering disciplines use multiple languages and tools whose results are not always easily integrated. The lack of integration is a source of design discrepancies and errors. The potential for MBSE is that it provides a means to integrate multi‐disciplinary engineering including systems, hardware, software, analysis, and test throughout the development life cycle. This panel will include representatives from other engineering disciplines to address questions such as: 1) What should other engineering disciplines expect from MBSE, and what should systems engineering expect from other disciplines to enable MBSE? 2) What can MBSE learn from model‐based approaches used in other engineering disciplines? 3) How should the practices and tools be integrated/coupled across disciplines? 4) How are the system, hardware, and software models managed to ensure an integrated technical baseline? 5) How should a program be organized to achieve more effective utilization and application of model‐based engineering? These are some of the questions that must be answered to more fully reap the benefits of MBSE.
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 | 0.000 | 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