XML-based modeling and simulation: meta-models are models too
Bibliographic record
Abstract
This article introduces multi-formalism modelling and meta-modelling to facilitate computer assisted modelling and simulation of complex systems. To aid in the automatic generation of multi-formalism modelling and simulation tools, formalisms are modelled in their own right, at a meta-level, within an appropriate formalism. This approach is implemented in the interactive tool ATOM3 (A Tool for Multi-formalism Meta-Modelling). This tool is used to describe formalisms commonly used in the simulation of dynamical systems, as well as to generate custom tools to process (create, edit, simulate, ...) models expressed in the corresponding formalism. ATOM3 relies on graph rewriting techniques to perform the transformations (modelled as graph grammars) between formalisms as well as for other tasks, such as code generation or simulator specification.The Finite State Automata (FSA) formalism is used to demonstrate the concepts of meta-modelling as well as model transformation (in particular, simulation of FSA models).The issue of a neutral model exchange and re-use format is addressed in the context of meta-modelling. Core XML is proposed as a standard external format. Thanks to the power of the meta-modelling approach, DTD, XMLSchema, and XSLT specifications may be replaced by models, externally represented in core XML, in appropriate formalisms (Entity Relationship for syntax and Graph Grammar for transformation respectively).
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".