A Survey on Decentralized Metaverse Using Blockchain and Web 3.0 Technologies, Applications, and More
Bibliographic record
Abstract
This survey delves into the convergence of blockchain, Web 3.0 technologies, and the decentralized metaverse, analyzing their combined effects on virtual experiences. The study meticulously examines the decentralized metaverse’s architecture, intrinsic properties, and transformative potential. Central to our analysis is the role of blockchain technology in addressing scalability issues and presenting practical applications in virtual real estate, gaming, and social interactions. Furthermore, we explore consensus mechanisms such as Proof of Work (PoW) and Proof of Stake (PoS), emphasizing their significance in the decentralized framework. The survey also investigates governance models and exceptionally Decentralized Autonomous Organizations (DAOs) and identifies associated challenges, including data security threats and possible mitigation strategies. By incorporating case studies on platforms like Decentraland, Vault Hill, and The Sandbox, we illustrate real-world implementations and emerging trends within the decentralized metaverse. This research highlights the profound implications of decentralized technologies on digital interactions, economies, and governance, marking a pivotal shift towards the Web 3.0 era. It underscores the potential for these technologies to redefine ownership, identity, and social engagement in virtual environments. Moreover, the paper outlines future research opportunities, encouraging further exploration into the integration and advancement of decentralized systems within the metaverse. The survey provides a comprehensive overview of the decentralized metaverse, supported by blockchain and Web 3.0 technologies. It offers valuable insights into the challenges and opportunities within this rapidly evolving domain, paving the way for innovative applications and research directions to shape the future of digital interaction and governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".