Guest Editorial: Ubiquitous Intelligence for Internet of Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As one of the prominent networking paradigms in the realm of the Internet of Things (IoT), the Internet of Vehicles (IoV) facilitates seamless information dissemination and task processing among vehicles equipped with onboard sensing, communication, computing, and storage capabilities. With the advancements in artificial intelligence (AI) techniques, there is a vision to achieve pervasive intelligence within the IoV ecosystem. Various network entities, including connected vehicles, wireless base stations, edge/cloud servers, and aerial/space-assisted devices (such as drones and satellites), are expected to interact efficiently to perceive, reason, and make intelligent decisions based on contextual awareness. These advancements aim to enhance networking and computing effectiveness. The realization of an intelligent, safe, and ubiquitous IoV heavily relies on highly responsive task computing, adaptive networking, and efficient resource control to meet the increasingly diverse requirements of vehicular applications.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it