STUDY OF SYSTEM INTERFACES THROUGH THE NOTION OF COMPLEMENTARITY
Bibliographic record
Abstract
Abstract Understanding emergence is an important goal of system thinking, as it can express both desirable and negative properties of products and systems. Emergence has also a special importance as it has a direct link to the performance of products and systems, and thus has a direct relationship with the quality of life and thus sustainability in our societies. Emergence and system thinking are closely related to engineering design methodologies. In our paper, we develop a more precise definition of emergence through the core principles of systems complementarity that are similarity, irreducibility and sophisticated relationships expressed through the interfaces between systems, subsystems or product components. We demonstrate the utility of the approach based on an aircraft pylon case study by presenting a detailed definition of an interface design matrix and analyse how pylon subsystems influence emergence. The results have shown that the product can be perfectly represented by a model-based approach supporting interface management and the assessment of system complementarity. In turn, this approach allows to go beyond a qualitative definition of emergence, as it proposes a quantitative approach through the assessment of complementarity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".