Metaverse Key Technologies and Blockchains: Impacts & Considerations
Bibliographic record
Abstract
As of October 2021, Facebook officially renamed itself Meta, and social networks and three-dimensional (3D) virtual worlds have adopted Metaverse as a new model. The metaverse offers consumers 3D immersive and individualized experiences through various innovative technologies. Although the metaverse is widely popular and beneficial, protecting user data and digital material is a natural concern where the transparency, decentralization, and immutability of blockchain make it a conclusive answer in this aspect. We intend to present the impact of blockchains on different metaverse key technologies and their applications. The work proves the technical challenges of the metaverse in each perspective and emphasizes the importance of blockchains. The article investigates blockchain's role in different metaverse enablers, such as immersive apps, digital twins (DTs), the Internet of Things (IoT), big data, and artificial intelligence (AI). This article shows the blockchain's role in metaverse applications and services. Finally, we describe promising directions for future study, innovation, and development that will enable blockchain to be applied to the metaverse.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".