Blockchain and IoT for Delivery Assurance on Supply Chain (BIDAS)
Bibliographic record
Abstract
Blockchain technology introduced immutable distributed ledgers to the technology landscape. With the current popularity and success of the blockchain technology, researchers are looking for further implementation opportunities for the tamper-free ledgers. The supply chain industry has been a beneficiary of blockchain technology with its rich set of participants. From financing all the way to tracking containers, there are opportunities in the supply chain industry to utilize distributed ledger technologies. Furthermore, Internet of Things (IoT) and wireless enabled devices add value by providing services based on their sensor capabilities. Our study is on the synergy of blockchain technology and IoT to provide quality data to supply chains. We believe that the next generation of IoT services would only be possible with the democratic autonomy of devices in an environment where privacy, trust, transparency, and security are provided. With blockchain and IoT, there is a potential in rearchitecting supply chain systems. With the addition of IoT capabilities, there are opportunities to create better business models.In this paper, we focus on delivery assurance in the supply chain industry, and we propose a novel blockchain-based transparent delivery framework for creating solutions that record and share data on the interaction of business participants. This framework helps create solutions that include handover and monitoring aspects of the delivery businesses and adds several benefits that come with the blockchain technology.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".