Formalizing cardinality‐based feature models and their specialization
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Bibliographic record
Abstract
Abstract Feature modeling is an important approach to capture the commonalities and variabilities in system families and product lines. Cardinality‐based feature modeling integrates a number of existing extensions of the original feature‐modeling notation from Feature‐Oriented Domain Analysis. Staged configuration is a process that allows the incremental configuration of cardinality‐based feature models. It can be achieved by performing a step‐wise specialization of the feature model. In this article, we argue that cardinality‐based feature models can be interpreted as a special class of context‐free grammars. We make this precise by specifying a translation from a feature model into a context‐free grammar. Consequently, we provide a semantic interpretation for cardinality‐based feature models by assigning an appropriate semantics to the language recognized by the corresponding grammar. Finally, we give an account on how feature model specialization can be formalized as transformations on the grammar equivalent of feature models. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.004 |
| 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