Spatial Framework for the Assessment of Road Traffic Accidents in Karachi
Bibliographic record
Abstract
Karachi, the most populous city of Pakistan, is entangled by an ever increasing health problem of Road Traffic Accidents (RTAs) in the recent past with ranked 4th in the world stats of highest road fatalities cities. The most devastating problem is that it is affecting the most productive age group. Nearly 300 RTA victims were interviewed last year and questioned about different socio-economic aspects of road crashes. This has demonstrated that the RTAs cases were observed between the age group of 18-45 years in Karachi. The problem is not only resulting financial losses but also social burden as well as pain, grief, psychological trauma in many cases and suffering for the effects which is certainly an irreparable damage. The prime objective of this study is to highlight the spatial variation most affected age group under threat of road traffic accidents in Karachi. This has been achieved by using a stratified random sampling technique and targeting the effects of RTA. Information was collected and analyzed and spatial pattern of RTAs in terms of accident location has been displayed with high and low RTA cases caused injuries and fatalities. Town based Road Accidents maps were developed in ArcGIS 10.1 to show the spatial patterns of road accidents. This study approaches with emphasis the miserable outcome of road accidents on the specific age grouped people because they are mostly involved in road crashes and also the future prospect of the country.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".