Roads in India
Bibliographic record
Abstract
BACKGROUND: The World Health Organization estimates that more than 15% of the global burden of road traffic trauma is in India. We performed an image-based survey of 3 major roadways in New Delhi, India, to evaluate collision-prone vehicle and pedestrian behaviors. METHODS: We used a cross-sectional survey design with photograph- and video-based data collection. The study was performed at 3 purposively sampled high traffic volume roadways in New Delhi, India. The authors reviewed preliminary photographs and came to a consensus pertaining to the definition and criteria for dangerous and collision-prone behaviors. Analysis was descriptive and was based on frequency data. RESULTS: A total of 11,214 subjects were evaluated. Eighty-six percent were vehicles (n = 9624), whereas the remaining 14% were pedestrians (n = 1572). In 99% of the frames, 1 or more predefined behavioral infraction was identified, with a total of 21% (n = 2392) of subjects committing these infractions. Specifically, 15% of all vehicles (n = 1468) and 59% of all pedestrians (n = 924) displayed a risk-taking infraction. CONCLUSIONS: Road users in New Delhi, India, engage in unacceptably high rates of collision-prone behavior. There is a need for interventions that will improve the behaviors of road users.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.011 |
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".