Blockchain and Metaverse For Peer-to-peer Energy Marketplace: Research Trends and Open Challenges
Bibliographic record
Abstract
Blockchain is an innovative technology destined to shape the future of businesses and markets around the globe. It promises the democratization of influence and transparency of information among its members. Academic researchers, as well as companies, are investing time, money, and effort to bring it to the market hoping that they maximize their projects' value. In this article, we take a look at the energy sector, specifically peer-to-peer (P2P) trading, which is moving rapidly towards digitization and integration of cutting-edge technologies, such as blockchain, to become smarter and more efficient like never before. Also, we investigate the feasibility and requirements of a blockchain-based P2P energy marketplace in the metaverse. Then, we discuss the current limitations of the P2P trading systems, the opportunities brought by blockchain to achieve market readiness, the current research trend, the existing pilot projects, and finally the open research challenges.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.025 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".