THE ANALYSIS OF TRAFFIC ACCIDENTS ON LITHUANIAN STATE ROADS
Bibliographic record
Abstract
A great number of serious road accidents occur all over the world every year. The problem of road traffic safety is still acute in spite of some progress in this area in recent years. Road traffic accidents depend on the following factors: road traffic volume, road traffic speed, weather conditions, driving experience and driving culture of drivers. All these factors are associated with traffic safety, human lives and health. The main goal of this work is to provide the statistical analysis of traffic accidents and investigate the causes, structure, dynamics and seasonal character of traffic accidents on Lithuanian state roads with various road pavements. The data on traffic accidents, traffic volume, traffic speed, as well as the number of injured and killed people and economic losses caused by traffic accidents on Lithuanian state roads in 2004 – 2011, provided by the Lithuanian Road Administration under the Ministry of Transport and Communications, Lithuanian Department of Statistics, Transport and Road Research Institute and Police Department under the Ministry of Interior are analysed.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".