Trauma in traffic in Montenegro between 2004 and 2010
Bibliographic record
Abstract
The first car showed up in Montenegro in 1902. There are 177 677 cars in Montenegro. Every third citizen owns a car. The total cost of the damage created by road accidents is estimated to 2% of the GDP, which makes the issue of traffic safety one of the issue with priority. The aim of this study is to make the trauma in traffic in Montenegro be seen so that the prevention programmes could be created. Method Data used was found in police reports in the given period. The data had been analysed, statistically processed and shown in the shape of a graph. Results 30% of cars is between 10 and 20 years old. About 41 000 cars is between 20 and 30 years old. Only a quarter of cars has the age that is less than 10 years. 12 913 persons were hurt in Montenegro from 2004 to 2010. 9559 (74%) person were slightly injured, 2830 (22%) were seriously injured and there were 524 (4%) of those who were dead. Average number of injured persons in road traffic accidents is 2152 per year. Death rate (deaths/100 000 persons) is 14/100 000 persons from given period. The drivers consist 46% (239) of cases of the total number of those killed. Co-drivers consist about 34% (177) and pedestrians consist about 20% of the cases (108). Conclusion By identifying the main sources of traffic accidents and applying all activities of prevention we can decrease the consequences of the things mentioned above.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".