Feasibility of Internet of Things and Agnostic Blockchain Technology Solutions: A Case in the Fisheries Supply Chain
Bibliographic record
Abstract
Along with Internet of Things -IoT-, distributed ledger/blockchain technology can provide substantial benefits to the management of supply chains. However the adoption of blockchain-based solutions may face substantial challenges such as scalability, which happens when several parties have to access and record information in a single blockchain. The use of two or more blockchains may help to mitigate such problem but it may require the use of an alternative architecture that enables interoperability. This work investigates the feasibility of adopting an agnostic blockchain architecture based on the particularities of the supply chain commonly found in the fisheries sector. A case in the fisheries sector in Atlantic Canada, characterized for the capture of different species destined for markets worldwide, is used to explore the adoption of an agnostic blockchain architecture. Emerging concepts such as agnotic blockchain applied to the supply chains of perishable goods may open the door to the development of innovative solutions.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".