Introduction to the Special Issue on Intelligent Applications of Web 3.0 and Metaverse for Connected Autonomous Vehicles: Part 2
Bibliographic record
Abstract
The convergence of Web 3.0 and Metaverse technologies with Connected Autonomous Vehicles (CAVs) is catalyzing a new era of intelligent vehicular systems, characterized by decentralization, immersive interaction, and enhanced autonomy. This paradigm is especially valuable in scenarios demanding secure peer-to-peer coordination, trustless automations, and seamless integration of physical and virtual environments under real-time constraints. Nonetheless, realizing such intelligent applications introduces critical challenges, including the development of robust decentralized governance and smart contracts, ensuring ultra-low-latency and high-throughput communications in edge computing contexts, achieving seamless digital–physical synchronization via high-fidelity digital twins or Metaverse representations, and guaranteeing scalability and privacy across distributed vehicle networks. This special issue brings together a collection of pioneering research that tackles these multifaceted challenges, showcasing innovations that advance the state of the art toward more secure, responsive, immersive, and decentralized CAV systems empowered by Web 3.0 and Metaverse technologies.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.096 | 0.050 |
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".