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
It is anticipated that vehicles having automated features of Level 0 to Level 5 will coexist in the future. However, many people are unsure what role, if any, human drivers will play at these levels. How do these automated features affect drivers' performances? This article attempts to answer this question by reviewing critical information from human-automation system characteristics of vehicles with specific automated features (AV). Essential facts about the differences in functional features between human drivers and systems and automated features at various levels were clarified and summarized, including their characteristics, roles, and technical AV structures. Finally, drivers’ performances at all automation levels were discussed. This review provides the insight needed to understand how the automated features affect drivers' performances and to what extent. The results indicate that drivers’ performance does not improve as the automated level upgrades. Compared with no automation, active-safety and high automation can achieve lower workload and better driving performances for drivers. In contrast, driver assistance and partial/conditional automation impose more increased workloads and unstable (even risky) driving performance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it