An ontological and hierarchical approach for supply chain event aggregation
Bibliographic record
Abstract
Time Petri Nets (TPN) have been applied to model the basic event patterns that arise commonly in supply chains. Additionally these TPN-specified patterns can be aggregated to create more complicated supply chain event systems. In our previous work, meanwhile, we introduced SCOPE (Situation Calculus Ontology for PEtri nets), which semantically describes Petri Nets using the Situation Calculus. In this paper, we show that TESCOPE, which extends SCOPE to incorporate the concept of time, can be naturally applied for supply chain event aggregation. That is, we show that supply-chain event patterns can be easily represented as TESCOPE-based Golog procedures, where Golog is a logic language built on top of the Situation Calculus; We further demonstrate by examples that these basic Golog procedures can be aggregated semantically and hierarchically into complex ones.
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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 | 0.000 |
| 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 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".