The AGILE4.0 MBSE-MDAO Development Framework: overview and assessment
Bibliographic record
Abstract
The EU-funded H2020 AGILE 4.0 project targets the enhancement and acceleration of processes for the development of complex aeronautical systems throughout multiple life-cycle stages, including design, production, certification and maintenance. In order to reach this ambition, the project Consortium has developed an original methodology and innovative digital technologies in the context of Model-Based Systems Engineering (MBSE) and Multidisciplinary Design Analysis and Optimization (MDAO). The methodology and the technologies are part of the AGILE 4.0 MBSE-MDAO Development Framework. This paper aims at presenting an overview of this framework, and assess its efficacy, i.e. demonstrate that the proposed framework improves the current state-of-the-art. Therefore, assessment metrics are identified and used to quantify how much the proposed methodology and digital technologies can effectively accelerate and enhance the development process of complex aeronautical systems.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".