Parse Trees and Unique Queries in Context-Free Parallel Communicating Grammar Systems ∗
Bibliographic record
Abstract
Parallel communicating grammar systems (PCGS) were introduced awhile ago purportedly to analyze concurrent systems on a language-theoretic level. To our knowledge however no actual relationship between PCGS and practical computing systems was ever investigated. We believe that PGCS with context-free components (CF-PCGS) have high practical potential, especially in the area of formal methods, so we start to bring CF-PCGS to a more practical level by studying a construct that has proven useful elsewhere: the parse tree. We can attach a parse forest (and then tree) to any CF-PCGS derivation in a natural way. However, the other way around (finding a derivation for each parse forest) holds only for one, very restrictive variant of CF-PCGS. Overall beside providing a convenient tool to be used in conjunction with CF-PCGS, this work strongly suggests the aforementioned PCGS variant as the most promising model for practical applications in general and for grammatical approaches to formal verification of concurrent, recursive systems in particular.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".