Bibliographic record
Abstract
Formal inference processes in all kinds of engineering designs are based on the cognitive process and means of abstraction. This paper presents a formal treatment of system design. It first discusses the differences of system designs in abstract and concrete systems. A hierarchical abstraction model of system descriptivity (HAMSD) is developed that explains the abstract levels of entities and artifacts in both the concrete world and the abstract world. The HAMSD model classifies the design of a system at five levels known as the levels of (1) analogue objects, (2) diagrams, (3) natural languages, (4) professional notations, and (5) mathematics from the bottom up. According to the HAMSD model, an architectural, functional, or behavioral design of a system should adopt a higher-level abstract means to describe the system at a given abstract level, in order to obtain more powerful descriptivity and higher accuracy. Therefore, a mechanical system may be designed by using diagrams; while an information or software system can not be precisely described by diagrams. The reason is that the former is at an abstract level lower than the descriptive means of diagrams, but not so is the latter. Applications of the above findings in dealing with complicated problems in large-scale software system designs are discussed in the context of software engineering. Real-time process algebra (RTPA) is adopted as a highly abstract means in case studies on formal methodologies of software system design and modeling for software objects and design patterns.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.037 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".