A Prospective Cloud-Connected Vehicle Information System for C-ITS Application Services in Connected Vehicle Environment
Bibliographic record
Abstract
In the era of the Internet of Things (IoT), various applications providing people with utilities are rapidly emerging by the needs for people. Recently, combining cloud computing, IoT technologies, and vehicular applications promotes Intelligent Transportation System (ITS). In other words, this is for safety of vehicles and drivers as well as convenience of the drivers. Vehicular Ad-hoc Network (VANET) is an application of Mobile Ad-hoc Network (MANET), which is a networking technology including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) using wireless communications. In real life, vehicles and infrastructures which have a lot of sensors generate various data for Cooperative-Intelligent Transportation System (C-ITS) application services according to each sensor type. Therefore, collecting, processing, and storing a number of data generated from various sensors built in vehicles and infrastructures require a great computing capacity and storage resources. In this paper, we propose an architecture of prospective cloud-connected vehicle information system for C-ITS application services in connected vehicle environment and describe the procedure of our local and global vehicle information system concerned with case scenario.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".