Method of Analysis of the Reasons and Consequences of Traffic Accidents in Uzbekistan Cities
Bibliographic record
Abstract
The article is devoted to the problems in the analysis of road safety in the cities of Uzbekistan, specifically addressing issues with the occurrence of traffic accidents and the analysis of their statistics.The purpose of the article is to study the relationship between violations of traffic rules and the occurrence of traffic accidents in Uzbekistan.This study used the statistical method of correlation analysis and revealed a linear correlation between factors such as the number of traffic violationsthe number of traffic accidents; the number of traffic violationsthe number of fatalities from traffic accidents; the number of traffic violationsthe number injured by traffic accidents; the number of traffic violationsthe number of traffic accidents with economic damage.To determine the degree of correlation between the number of violations of traffic rules, the number of road traffic accidents and their consequences, the authors used the coefficient of determination.The results of the study showed that the number of traffic violations is negatively correlated with the number and consequences of traffic accidents.The authors argue that the methodology for registering a traffic accident in Uzbekistan requires modification and that a traffic accident is affected not only by violations of the rules of the road for drivers but also by other factors, such as the design of elements of the road traffic network.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".