Bibliographic record
Abstract
A common way to represent organizational domains is the use of business process models. A Workflow-net (WF-net) is an application of Petri Nets (with additional rules) that model business process behavior. However, the use of WF-nets to model business processes has some shortcomings. In particular, no rules exist beyond the general constraints of WF-nets to guide the mapping of an actual process into a net. Syntactically correct WF-nets may provide meaningful models of how organizations conduct their business processes. Moreover, the processes represented by these nets may not be feasible to execute or reach their business goals when executed. In this paper, the authors propose a set of rules for mapping the domain in which a process operates into a WF-net, which they derived by attaching ontological semantics to WF-nets. The rules guide the construction of WF-nets, which are meaningful in that their nodes and transitions are directly related to the modeled (business) domains. Furthermore, the proposed semantics imposes on the process models constraints that guide the development of valid process models, namely, models that assure that the process can accomplish its goal when executed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".