Towards Efficient and Fine-Grained Traceability for a Live Lobster Supply Chain using Blockchain Technology
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
Product traceability has become essential in modern supply chain management, ensuring product safety, quality, and transparency. However, implementing traceability, especially for live biological products managed by Small and Medium Enterprises (SMEs) with limited resources, presents challenges. The balance between traceability performance and costs is critical for adoption. Recently, the integration of Internet of Things (IoT) and Blockchain technology has shown promise in revolutionizing traceability systems, offering unprecedented data granularity and integrity. Yet, few studies explore these technologies’ design within SME-dominated, live product supply chains. Addressing this gap, our study introduces a novel technological architecture and three data validation models: lightweight, detailed, and intermediate. We evaluated these models in a Canadian seafood supply chain, focusing on live lobster products, using simulation platforms. Our findings highlight a trade-off between traceability and operational costs, with the intermediate solution offering promising benefits without compromising cost-effectiveness.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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