Traceability information modeling and system implementation in Chinese domestic sheep meat supply chains
Bibliographic record
Abstract
Abstract Traceability has become a prerequisite for ensuring meat quality and safety. It is the most critical requirement for developing a well‐structured traceability system to identify efficiently and model the traceability information need to be captured. This article identified Chinese domestic sheep meat supply chain and proposed the Petri nets and UML based modeling approach for identified traceability information and process. This approach followed the definition of state and transition in sheep meat production process. An in‐depth analysis was conducted for the sheep stakeholders and production flow of the sheep meat chain. The state‐transition for the production process and specific traceability information need to be captured were identified including the product, process, quality, and transformation information. Furthermore, traceability functionality requirement and system architecture were presented. System evaluation results illustrate the model can help to provide guidance for standardizing meat product flow and supporting quality traceability management in meat industry. The system can provide making‐decision for the meat product quality and safety control. Practical applications The proposed method could be applied widely in development of traceability system and provided making‐decision for the control of meat product quality and safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".