RosettaNet-Based Implementation of CPFR Using Semantic Web Services
Bibliographic record
Abstract
Synergistic effect and competitive pressure drive firms to participate in supply chain coordination which depends on the alignment of business goals, interaction processes and information flow across the supply chain. This paper presents the implementation of supply chain coordination focusing on CPFR using RosettaNet and semantic Web services. We use CPFR to define the closed-loop functionality connection within supply chain and guarantee that customers' demands are the source and goal of multi-partners coordination. Further, we utilize the Rosettanet PIPs to create the sharing process templates of CPFR, and map business logic, message flow, actions and message contents into composition rule, composition process, Web services and message of Web service interactions respectively so that it is easy to specify the roadmap of Web service composition and improve the efficiency of Web service composition. Besides, we formularize the composition of web services. Finally, we design the architecture of server according to three interoperability levels: message, process and business.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".