Describing Pattern Languages for Checking Design Models
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Bibliographic record
Abstract
Many designers use the patterns of a pattern language in creating the design model. In designing with patterns, there are three aspects of the pattern language that must be taken into consideration: structural, syntactic, and semantic. That means, the patterns must be applied correctly, the relationship between patterns must be correct, and the design model must be semantically correct. The syntactic aspect is important for pattern languages due to the fact that the patterns in a pattern language are interconnected via several relationships. To achieve automatic design model checking, the three aspects of a pattern language must be precisely defined. We propose formalisms for representing the structural, syntactic, and semantic aspects of a pattern language. As our case study, we select a pattern language in the domain of enterprise application architecture, and show how the pattern language is described using the proposed formalism.
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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