A Reliable Traceability Model for Grain and Oil Quality Safety Based on Blockchain and Industrial Internet
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
Gain and oil are important compounds in global food supplies, and ensuring the quality and safety of grains and oil is a critical issue in the food supply chain security. Data traceability is the key factor in quality and safety management. Currently, it is a big challenge to ensure the reliability of data and guarantee the efficient exchange of data in various highly heterogeneous systems. To address this challenge, we develop a reliable traceability model applied to the grain and oil industry. In this paper, we first analyze the characteristics of the whole chain traceability information flow, and then we propose the concept that the connector for blockchain and industrial internet is suitable for data traceability in the grain and oil industry. Based on this concept, a reliable traceability model of grain and oil quality and safety is constructed. Finally, a reliable traceability prototype system for wheat quality and safety was designed, and the system implementation of the model was validated. The overall advantage of the proposed model is that the traceability information is safe and credible, the interaction is concise and efficient, and the security and full-process traceability of cross-chain information interaction are guaranteed. This paper fills the gap in the application of research chain network in the field of grain and oil traceability. Reference to this model can also be used to implement and adjust the traceability system, which is adaptable to stakeholders in the grain and oil industry. The model and techniques in this paper not only demonstrate value in real-world applications but also inspire further research in the field.
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.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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