Specificity analysis of safety enhancement for rural roads in China
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
Rural roads are an important component of highway network and the major infrastructure for the farmers' production and life. In recent years, the traffic environment of rural roads has changed dramatically, and the safety problems of rural roads have become more and more prominent in China. The proportion of accidents on rural roads increased year by year. The severe road accidents with lots of casualties happened frequently. The farmers have become the largest victim group of road accidents in China. The factors that leading to road safety problems of rural roads includes: (1) the road safety awareness of farmers is relatively weak; (2) the safety performance of motor vehicles is relatively poor; (3) the safety facilities on rural roads lack relatively; (4) the road safety management of rural roads lags behind. It is very urgent to implement the Highway Safety Enhancement Projects (HSEP) on rural roads. Due to the specificity of rural roads, the existing technical measures cannot be directly applied to the HSEP for rural roads. These specificities includes: investment, road environment, road users, vehicles, maintenance responsibility, traffic safety management, and engineering construction. New low-cost technical measures must be adopted or invented to fit the limit of the budget and the safety requirements.
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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 | 0.001 |
| 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 it