IntelliChain: An Intelligent and Scalable Framework for Decentralized Applications on Public Blockchain Technologies: An NFT Marketplace Case Study
Bibliographic record
Abstract
Non-fungible tokens (NFTs) have been attracting the interest of both technical and non-technical parties, including collectors and traders, among others. The number of transactions in NFTs surpassed $50 billion in 2022. Blockchain technology’s advantages as a distributed, immutable, and transparent database make it ideal for verifying the ownership of digital goods created by their producers. On the other hand, high computation and transaction costs are known disadvantages of public blockchain networks like Ethereum V1, used in NFT marketplaces. To address these inefficiencies, other public blockchain systems have emerged as replacements for NFT marketplaces, each with its own unique properties. When planning such an NFT exchange, it is vital, but not trivial, to select the most appropriate public blockchain platform. In this work, we make two contributions to support this decision. In this paper, we present IntelliChain , a self-adaptive framework that can predict the best optimal transaction fees (also known as a gas fee) for blockchain to reduce the errors and also an ability to switch the public blockchain-based dynamic needs such as transaction fees and stability of the network.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".