Hierarchies of place/transition refinements in Petri nets
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
Place and transition refinements provide a convenient method of structuring complex net models by replacing single elements (places and transitions) at a "higher-level" of abstraction with "lower-level", more detailed, subnets. The concepts of static and dynamic place/transition refinements are introduced. Dynamic refinements do not increase the size of the (refined) model because no "expansion" of the model is performed; instead, only a "logical" association of higher-level elements with lower-level subnets is maintained and used in model analysis. Multiple applications of place/transition refinements results in hierarchical net models. The paper formalizes the concept of hierarchies of refinements in Petri nets and shows simple applications of the hierarchical approach to modeling of manufacturing cells.
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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 | 0.001 |
| 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