Analysis on Deaths Due to Traffic Accidents Among Residents in Zhucheng City,2010
Bibliographic record
Abstract
[Objective]To analyze the characteristics of deaths induced by traffic accidents in Zhucheng city,so as to provide basis for the decision-making on prevention and control of road traffic accidents.[Methods]Analysis was made on the traffic accident deaths in Zhucheng residents,2010.[Results]The mortality of traffic accidents in Zhucheng residents in 2010 was 32.52/105,with male mortality of 47.13/105,and female mortality of 17.73/105.Among these traffic accident deaths,the mortality of residents at 0 to 19 years old was 6.96/105,that of 20 to 29 years old was 38.78/105,that of 30 to 39 years old was 42.32/105,that of 40 to 49 years old was 45.11/105,that of 50 to 59 years old was 35.52/105,that of 60 to 69 years od was 46.31/105,and that of ≥70 years old was 30.07/105.The total traffic accident deaths were 354,among them,those riding motorcycles accounted for 30.79%,those walking accounted for 25.99%,those riding electric bicycle accounted for 11.58%,those driving other motor vehicles accounted for 8.76%,those hitchhiking motor vehicles accounted for 9.89%,those riding bicycles accounted for 10.45%,those hitchhiking electric bicycles accounted for 0.85%,those of unknown reasons accounted for 1.41%,and those hitchhiking bicycles accounted for 0.28%.The deaths induced by traffic accidents in the first quarter of 2010 accounted for 28.21% of the total deaths,those in the second quarter accoun-ted for 20.90%,those in the third quarter accounted for 20.90%,and those in the fourth quarter accounted for 29.94%.And the deaths induced by traffic accidents in downtown accounted for 27.40%,and those in countryside accounted for 72.60%.[Conclusion]The mortality of traffic accidents in Zhucheng is relatively high in 2010,and young adults,male,motorcycle drivers are the main victims of traffic accidents.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 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".