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.
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.
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 | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".