Relative Observability of Discrete-Event Systems and its Supremal\n Sublanguages
Bibliographic record
Abstract
We identify a new observability concept, called relative observability, in\nsupervisory control of discrete-event systems under partial observation. A\nfixed, ambient language is given, relative to which observability is tested.\nRelative observability is stronger than observability, but enjoys the important\nproperty that it is preserved under set union; hence there exists the supremal\nrelatively observable sublanguage of a given language. Relative observability\nis weaker than normality, and thus yields, when combined with controllability,\na generally larger controlled behavior; in particular, no constraint is imposed\nthat only observable controllable events may be disabled. We design algorithms\nwhich compute the supremal relatively observable (and controllable) sublanguage\nof a given language, which is generally larger than the normal counterparts. We\ndemonstrate the new observability concept and algorithms with a Guideway and an\nAGV example.\n
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".