IPBlocks: A Blockchain Ecosystem for Secure IP Registration and Decentralized Marketplace
Bibliographic record
Abstract
Intellectual properties (IPs) are crucial intangible assets in today's innovator economy. However, their management suffers from bureaucratic inefficiencies and vulnerability to mismanagement. We introduce IPBlocks, a blockchain-based solution that streamlines IP applications, trading, and royalty transfers through a decentralized marketplace. We develop a set of algorithms that allow users to apply for, publish, auction, and transfer IPs. These processes are designed to ensure enhanced security and transparency. The algorithms are implemented through Solidity on the Ethereum platform. These algorithms exist in a smart contract that is deployed on a blockchain network to interface with a user-friendly web application. Large files associated with IPs are stored in a decentralized file storage system. Our performance analysis demonstrates the system's feasibility and scalability for large user bases, testing the performance in local and deployed settings. IPBlocks aims to revolutionize IP ownership and management by reducing processing times and improving royalty distribution. We opensource our codebase to the community to promote collaborative development and transparency.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".