Bibliographic record
Abstract
The level of investment in AVs technology has been increasing over the years as both researchers and developers are cooperating with the objective of developing AVs and understanding their behaviors and implications. Despite the enthusiastic speculation about AVs, little is known about the implications of AVs on our lives and the intertwined relationships between the implications. Thus, the main objective of this paper is to reveal the benefits and risks of AVs and sketch out the main trends in this area in order to provide some directions and recommendations for the future. This study focuses on analyzing the impact of AVs on the required fleet size, vehicle utilization, cost of mobility, public transit service, public behavior, transportation network, land use, economy, environment, society, and public health. Furthermore, the paper analyzes the intertwined relationship between the implications of AVs. Additionally, the paper sheds light on the potential benefits and challenges of the deployment of AVs in developing countries. The analysis shows that while AVs offer multiple benefits, they also pose new risks. The degree to which AVs can affect our plant mainly depends on regulatory actions, as the broader implications of AVs are mainly dependent on how the technology will be adopted, which can be controlled by regulatory actions. Doi: 10.28991/CEJ-SP2021-07-06 Full Text: PDF
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".