Lateral placement distribution of vehicles at horizontal curves on two-way undivided roads
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
The majority of road crashes occur at horizontal curves due to the inability of drivers to keep their vehicles at the right lateral position across the carriageway width. The lateral placement profile, which is a good indicator of crash potential at a horizontal curve, is further governed by the subject vehicle category, road geometry, and lane type. With the change of the aforementioned variables, the lateral placement of vehicles fluctuates widely across the carriageway of the horizontal curves. On this background, the present study forwards an approach to collect the lateral placement data at horizontal curves, investigates its characteristics using the fitted distribution models, and examines the influence of subject vehicle category, lane type, and curve radius on the lateral placement. The outcomes of this study explain the driving behaviours of different vehicles while manoeuvring at the horizontal curve, which is considered important prerequisite information for assessing traffic safety at horizontal curves.
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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 | 0.000 | 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) | 0.000 | 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