Computer automated multi-paradigm modelling: meta-modelling and graph transformation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Computer automated multi-paradigm modelling based on meta-modelling and graph transformation is presented. The syntax of a class of models of interest is graphically meta-modelled in an appropriate formalism such as entity-relationship diagrams. From this abstract syntax, augmented with concrete (visual) information, an interactive, visual modelling environment is generated. As the abstract syntax of all models is graph-like, graph rewriting is used to perform model transformation. Graph grammar models thus allow for model transformation specification. Graph rewriting provides a rigourous basis for specifying and analyzing model transformations such as simplification, simulation, and code generation. AToM/sup 3/, a tool for multi-formalism and meta-modelling, is introduced. Meta-modelling and graph transformation concepts are introduced through a simple reactive system example: a timed automata model of a traffic light. Meta-modelling, generating the visual modelling environment, and modelling transformations as graph grammars, as well as executing them, are performed in AToM/sup 3/. The model transformations include simulation, transformation into timed transition Petri nets, and code generation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it