Template semantics: a parameterized approach to semantics-based model compilation
Bibliographic record
Abstract
This dissertation discusses a parameterized approach to the compiling of model-based notations into input languages of formal-analysis tools, based on descriptions of the notations' semantics. The semantics of a model-based notation is complex, and formalizing it in a semantics-description language, such as structural operational semantics and higher-order logic, can be challenging and error-prone. We propose a new approach, called template semantics, to structure the semantics of model-based specification notations. We demonstrate how to use template-semantics descriptions to construct notation-specific model compilers, which ease the mapping of new notations or notation variants to analysis tools. The basic computation model of template semantics is a non-concurrent, hierarchical transition system (HTS), whose execution semantics are parameterized. Semantics that are common among notations, e.g., the concept of an enabled transition are captured in the template, and a notation's distinct semantics, e.g., which events can enable transitions, are specified as parameters. HTSs can be combined by composition operators to form more complex, concurrent specifications. We provide the template semantics of seven composition operators and some of their variants; the operators define how multiple HTSs execute concurrently and how they communicate and synchronize with each other by exchanging events and data. The definitions of these operators use the template parameters to preserve notation-specific behaviour in composition. By separating a notation's step semantics from its composition operators, we simplify the definitions of both.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".