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
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 metalevel, within an appropriate formalism. This approach is implemented in the interactive tool ATOM/sup 3/ (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, etc.) models expressed in the corresponding formalism. ATOM/sup 3/ 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).
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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