Research on the influencing factors of traceability information sharing of agricultural product supply chain under the background of blockchain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Increasing customer apprehensions regarding the security and nutritional value of agricultural goods are compelling governments and industries to implement traceable, transparent, and reputable logistics management systems. Blockchain-based agricultural logistics management systems guarantee the permanence of data once it is uploaded but cannot cope with the risk of data being falsified before uploading to the blockchain. In this work, we developed a collaborative game model between government bodies and agricultural enterprises based on the evolutionary game theory and explored the influencing factors of enterprises following the rules to share the real traceability information through numerical simulation using MATLAB. The findings show that government incentives and penalties promote positive behavior, and consumer and media supervision contribute to supply chain transparency, but firms tend to share truthful information only when it benefits them. This study builds upon existing research on the impact of social variables on both members' decision-making behavior. It highlights the positive roles of consumers and the media in the supervision of agricultural product traceability, which can help to raise public awareness of social responsibility and thus promote positive interaction in the market.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it