General Untimed Sequential Automata Models for the General Components of Automatic Transport Systems with Accumulation Areas
Bibliographic record
Abstract
Discrete event systems have been researched for quite a while in the domain literature. A series of modelling and analysis techniques have been used. Sequential automata models are at the basis of their studies and theories have been born to provide tools for their design. Automatic transport systems with accumulation areas are a particular type of systems where the sequential automata model can be successfully used. The aim of this paper is to establish connections between a sequential automata model and a Petri net model: first for a node with ";one"; input and ";m"; outputs and the second for a node with ";n "; inputs and ";m "; outputs, using basic elements of the automatic transport systems with accumulation areas. General models for an automatic transport system with accumulation areas are obtain by composing sub-models. In this paper, untimed sequential automata and untimed Petri nets have been used for modelling, time being not a defining element for this modelling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".