6.2.1 A fresh view on model‐based systems engineering: The processing system paradigm
Bibliographic record
Abstract
Abstract Model‐based systems engineering and graphical notations have an enormous potential for increasing design productivity, system quality and lifetime by shifting the bulk of design efforts to early phases. In spite of that this is hardly questioned, the shift towards model‐based approaches has not come to a break through, as we are experiencing in software engineering. It is believed that a major reason is lack of a common system view that can act as a framework for developing modelling languages and methods for a broad community of system engineers. This paper suggests such a framework. It identifies the systems of concern as a processing system that consist of a process control system and a resource system, and applies two related views on processing systems: A functional view and a solution view. The framework has been successfully tested on telecommunication systems and networks for some years. It is believed that it holds for many other system domains as well.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.010 | 0.026 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".