Agricultural Lightweight Embedded Blockchain System: A Case Study in Olive Oil
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
In Tunisia, one of the major problems of the olive oil industry is marketing. Several factors have an impact, such as quality, originality, lobbying, subsidies and the certification of extra virgin olive oil. The major problem remains the traceability of the production process to guarantee the origin of the food at all times. This fine-grained traceability can be achieved by applying Blockchain technologies. Blockchain can be used as a solution that could bring visibility to the oil supply chain. It is proposed in order to guarantee the veracity of the product information at different stages. In this paper, a multi-Blockchain, multi-sensor traceability system using IoT will be presented. Two Blockchains that can be programmed via Smart Contract will be used. The first one is Quorum, which is a private Blockchain used by the actors of our system, and the second one is Ethereum, which is public and connects the different actors who have access to our system. This smart contract allows us to conta our system to track the olive oil manufacturing process from the farmer, through the oil mill, the transporter and the quality controller to the customer. A general approach for managing the olive oil supply chain is presented. This approach offers the possibility for the system to be configurable. It is based on smart contracts and applications that interact with the same smart contracts. The IoT is used to configure sensors. These sensors are the source of data for the supply chain process. These sensors are connected to the embedded platforms that host Quorum.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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