Design and Implementation of Blockchain-based Anti-Counterfeit Traceability System for Beef Cattle Products
Bibliographic record
Abstract
Aiming at beef cattle product quality safety, the traditional anti-counterfeit traceability methods have serious data centering. To guarantee data security and reliability, this paper adopts blockchain technology with traceability characteristics, takes beef cattle products as the research object, constructs a supply chain traceability model of beef cattle products based on blockchain technology, and builds an anti-counterfeiting traceability system based on Hyperledger Fabric platform. The organizations at the management end of the same supply chain use the snowflake algorithm to generate corresponding IDs, which are interrelated with each otherand then combine the traceability ID, blockchain, and QR code to realize anti-counterfeiting traceability, finally complete data verification between the traceability ID and local information and return relevant information. At the same time, to guarantee the security of the QR code, the improved RSA algorithm is used to generate the key pair, the public key is used for encryption, and the private key is used to generate the encrypted QR code for the traceability ID, and the consumer can obtain the traceability ID by scanning the code and decrypting it. In order to verify the effectiveness of the RSA algorithm and the performance of the anti-counterfeiting traceability system, the system is tested and applied in this paper, and the test results show that the anti-counterfeiting function of the traceability system is realized, and the system performs well without the phenomenon of chain code collapse. Meanwhile, it is found that the efficiency of the consensus algorithm of various organizations needs to be improved, to ensure anti-counterfeiting traceability.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".