Transparent distribution system design of halal beef supply chain
Bibliographic record
Abstract
Halal food is food whose halal status is regulated by sharia institutions such as LPPOM MUI which is set by the government. The halal status of food must be traced from the process of raw materials, processing, packaging, transportation, and distribution to the final consumer. It is difficult to trace the certainty of halal food, especially beef in Medan City because the supply chain information from upstream to downstream is not transparent. To increase the transparency of beef status and increase consumer confidence, especially Muslim consumers, a distributed and transparent system is needed, where many parties can access the status of food at any time. So Blockchain technology is used to help track the halal status of beef along the supply chain. The purpose of this study is to design a system to obtain information certainty that beef distributed along the supply chain is halal and safe for consumption by utilizing Blockchain technology and to increase public safety and trust in the LPPOM MUI halal certification system. Based on the discussion and research analysis, it is known that the information in the halal beef supply chain in Medan City uses blockchain technology designed with a data security system using smart contracts, where information that has been stored cannot be changed by any party. so that there is a guarantee of information security in the beef supply chain in the city of Medan. This research is expected to support transparency, security, and certainty of information about halal beef in Medan City.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".