Agent-Directed Simulation and Nature-Inspired Modeling for Cyber-Physical Systems Engineering
Bibliographic record
Abstract
The definition of cyber-physical systemsCyber-Physical Systems (CPS) is revised from the point of view of the evolution of physical toolsEvolution of physical tools. The desirable synergies and contributions of the following disciplines are elaborated to tackle the complexity of cyber-physical systemsCyber-Physical Systems (CPS): simulationSimulation, agent-directed simulationAgent-directed simulation, and systems engineeringSystems engineering, including simulation-based cyber-physical systemsCyber-Physical Systems (CPS) engineeringCyber-Physical Systems Engineering. Over 80 types of systems engineeringSystems engineering are also listed. The richness of paradigms offered by nature-inspired modelingNature-inspired modeling and computing are elaborated on and sources of information, as well as over 60 possibilities offered by nature-inspired modeling, for simulation-based cyber-physical systemsCyber-Physical Systems (CPS) engineeringCyber-Physical Systems Engineering are pointed out. Some other important possibilities, such as cloud computation, big data analytics, cyber security, and ethical issues are treated in other chapters of the book and are not elaborated in this chapter.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".