Guest Editorial Introduction to the Special Section on Vehicular Networks in the Era of 6G: End-Edge-Cloud Orchestrated Intelligence
Bibliographic record
Abstract
The articles in this special section focus on vehicular networks in the era of 6G mobile communication. With the growth of the vehicle population, vehicular networks play a key role in building safe, efficient, and intelligent transport systems and has been attracting a lot of attention from both academic and industrial communities around the world. The rise of autonomous driving technology and the prosperity of mobile applications, e.g., real-time video analytic, image-aided navigation, natural language processing, and etc, have brought tremendous pressure on current vehicular networks, e.g., high bandwidth, ultra-low latency, high reliability, high security, powerful computation capability, and massive connections. It is necessary to continue to develop vehicular networks by combining the latest research intends in other fields to meet quickly rising communication and computation demands. The upcoming 6G technology, which provides Holographic and Artificial Intelligence (AI) enabled communications, together with the increasing implementation of artificial intelligence in mobile devices, will lead to a new research trend to end-edge-cloud orchestrated computing with intelligence. It means that, not only the intelligent communication protocols, but also the intelligent computing resource management and machine learning algorithms among the mobile vehicles, the edge and the cloud, should be redesigned to support the development of vehicular networks.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".