Flexible verification of user-defined semantic constraints in modelling tools
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
Many modelling tools embed verification rules that are checked against user-defined models to ensure they satisfy the static semantic constraints of the modelling language. However, there are many other contexts where required constraints vary with the intended purpose of the model, and not just the modelling language used. In this paper, we propose a flexible and practical approach for users to define, select, store, group, exchange, enable, and verify custom semantic constraints on metamodels with the Object Constraint Language. We illustrate the benefits of this approach with extensions to an Eclipse-based modelling tool, called jUCMNav, and applications to various contexts such as style compliance, analysis, and transformations that involve chains of tools. We believe this approach to be easily adaptable to other Eclipse-based modelling tools, which could then enjoy similar benefits.
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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 | 0.000 |
| 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