The reference system in product generation engineering: structuring reference system elements for advanced systems engineering based on the system triple
Bibliographic record
Abstract
Abstract Product engineering in general and advanced systems engineering in specific are highly complex and unique processes that strive to deliver innovations – successful new products. To reduce risk and time, product engineers refer to existing (socio-)technical systems or subsystems. These references are part of the reference system. A great variety of elements can be used as reference system elements in engineering projects, but the different types of reference system elements and their roles are not yet characterized. However, this is a necessary prerequisite to model and conduct product generation engineering effectively. Here, we show how reference system elements can be categorized into three types that differ regarding their intended application in the actual engineering project. Therefore, we introduce three subsystems: reference system of objectives, reference operation system, and reference system of objects. Furthermore, we provide definitions for all subsystems to specify the allocation. We believe these results will form the basis for a continuous description and continuous engineering of consecutive and parallel product generations based on model-based systems engineering. Furthermore, the results will be the starting point for the development of design supports to assist engineers in designing their specific reference systems and to make the reference system part of efficient engineering processes.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".