Analysis of Arbitrarily Large Networks of Discrete-Event Systems
Why this work is in the frame
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Bibliographic record
Abstract
Many engineering systems can be usefully modelled as networks of interacting, isomorphic, finite-state discrete-event systems. Examples include communication and transportation networks. For practical purposes, the number of subsystems is often arbitrary. In such cases, key problems of analysis are generally undecidable; however, inductive semidecision procedures can be formulated for checking whether networks of arbitrary size are equivalent to networks of bounded size. The appropriate notion of equivalence may vary, depending on the properties being analyzed. We examine a range of possible equivalences, identify system properties that they preserve, and show that semidecision procedures exist for checking these equivalences. On the other hand, we show that equivalence of networks to networks of bounded size is undecidable for a broad range of process equivalences, even for the simple network topologies of rings and line segments.
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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 | 0.001 |
| 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