Demographic Analysis of Automobile Accidents in Irbid, Jordan, Using GIS and GPS
Bibliographic record
Abstract
Both geographic information systems (GIS) and global positioning systems (GPS) are becoming common techniques in transportation studies, including the study of traffic issues in recent years. The research documented here aims to apply these techniques to traffic accidents in the city of Irbid, located about 100 km north of Amman, Jordan. With the help of GPS, 415 accidents occurring between April 2007 and March 2008 were located. Three main attributes of traffic accidents in the study area were observed: age of people causing the accident, types of vehicles involved in the accident, and cause(s) of the accident. The study revealed that most people who caused accidents were between 38 and 47 years old; more than 56 % of the accidents involved private cars. The study also revealed that failure to fasten seatbelts, faulty brakes, and broken/malfunctioning front and rear lights were the most common causes of accidents. The research divided the accident sites into those with very low, low, medium, high, and ve...
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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 | 0.002 | 0.002 |
| 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 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".