TRAFFIC SAFETY AND DRIVER EDUCATION IN SAN JUAN ARGENTINA
Bibliographic record
Abstract
With over 8,100 traffic fatalities in 1997 and an accident rate per 100 million vehicle kilometres travelled, approximately five times that of United States, Argentinean road authorities are now beginning to focus attention on traffic safety and driver education. One of the main problems in the search of causes for car accidents in Argentina is the lack of a reliable and updated data base. The results and conclusions presented in this paper are based on a thorough analysis of car accidents in the Province of San Juan, Argentina. A seven-year data base of car accidents has been compiled from police reports, including the results of traffic counts at intersections and other collision locations. In addition, topographic and filmed reports of such places and their surroundings bring about parameters such as stop lines, visibility triangles, road size, traffic light performance, etc., which allow to carrying out of a traffic flow analysis for proposing measures aiming to minimize accidents. For San Juan province, in general, the main causes are: high absolute car speeds, speed differences between vehicles, lack of good lighting, poor driving habits, lack of traffic control devices such as signs, signals, and an absence of road markings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".