Computer automated multi-paradigm modelling for analysis and design of traffic networks
Bibliographic record
Abstract
In this article, Computer Automated Multi-Paradigm Modelling (CAMPaM) is presented as an enabler for domain-specific analysis and design of complex systems. Traffic, a new visual formalism tailored to the domain of vehicle traffic networks, is introduced. In the CAMPaM approach, the syntax of Traffic models is meta-modelled in an appropriate formalism such as Entity-Relationship Diagrams. From this description of abstract syntax, augmented with concrete (visual) syntax information, an interactive, visual modelling environment is automatically generated. The semantics of the Traffic formalism is subsequently modelled by mapping Traffic models onto Petri Net models. As the abstract syntax of models, irrespective of the formalism they are described in, is graph-like, graph rewriting can be used to transform models. Graph Grammar models thus allow for the specification of model transformations. The meta-modelling and transforma-tion of the Traffic formalism uses our CAMPaM tool AToM A Tool for Multi-formalism and Meta-Modelling. The advantages of creating a domain-specific formalism such as Traffic as opposed to using a generic formalism such as Petri Nets are presented. We also demonstrate how mapping Trafc models onto Petri Net models allows one to employ the vast array of Petri Net analysis techniques. In particular, a Coverability Graph is automatically generated and conservation analysis is automated by transforming this graph into an integer linear programming specification which is subsequently solved by the lp_solve code.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".