FTG+PM: Describing Engineering Processes in Multi-Paradigm Modelling
Bibliographic record
Abstract
Model-based System Engineering (MBSE) is a methodology that uses models throughout the engineering to replace the paper-based approach of communication among stakeholders. Multi-Paradigm Modelling (MPM) is at the core of this engineering approach as for each phase in the engineering process the most appropriate models at the most appropriate levels of abstraction is used. A design process includes a set of activities in which the design decisions or evaluations of the (sub-) system properties are done. Furthermore, the design artifacts are transformed by the design activities.We can define transformations as the manipulation of a model with a specific purpose. MPM approaches do not have a standard way of representing processes. A process model for MPM should focus on the languages, model instances and transformations between these models at different levels of abstraction. In this chapter, we propose the Formalism Transformation Graph and Process Model (FTG+PM) as a standard representation of MPM processes. The described process can be simulated for analysis and orchestration, as a set of (automatic) transformations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".