Comments on “Feedback Control Logic for Forbidden-State Problems of Marked Graphs: Application to a Real Manufacturing System”
Bibliographic record
Abstract
The AIP manufacturing testbed discussed by Ghaffari et al. (see ibid., vol. 48, p.18-29, 2003) was previously treated using automaton-based [as distinct from Petri net (PN)-based] supervisory control theory (SCT). Problems of this general type have been transcribed from the PN literature, sometimes greatly enlarged, converted into SCT format, and solved using integer-decision-diagram (IDD) techniques with only modest expenditure of time and memory. Thus it is substantially misleading to state, that "the AIP control problem would require huge processing time and memory if addressed with the Ramadge-Wonham [SCT] approach." In contrast to the restriction in Ghaffari et al. (2003) to "marked graphs", no prior structural restrictions other than finiteness of the state set (or boundedness in the case of PNs) were placed on the models of system components.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".