Analysis on hazard perception ability of drivers in plateau areas: by different elevations
Bibliographic record
Abstract
The low-pressure and low-oxygen environment of plateaus results in a greater workload for drivers, which contributes to the serious road safety situation in plateau areas in China. This study conducted four hazard perception experiments in Nanjing (50 m above sea level (asl)), Nyingchi (2995 m asl), Lhasa (3650 m asl), Nagqu (4460 m asl), and Yanghu Scenic Spot (4998 m asl) using UC-WIN/ROAD driving simulation software. A total of 31 drivers (23 males, 8 females) were recruited in this study, with a mean age of 28.0 years old and mean driving experience of 6.5 years. A bivariate correlation test was adopted to analyze the impacts of the elevation, age, acclimation period, gender, and driving experience on the perception–reaction time. Then, the drivers were divided into three groups (Good, Medium, and Poor) using K-means clustering. Finally, the marginal effects of the linear regression models for the three groups were calculated to augment the comparison. As expected, the elevation showed a positive correlation with the perception–reaction time but indicated a variance in influence effects in the three groups. Consistent with previous evidence, experienced drivers had better perception ability in the plateau. These findings could help in the design of high-elevation roads and guide the definition of suitability to drive in the plateau, thereby improving driving safety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".