Vulnerable road-user deaths in Brazil: a Bayesian hierarchical model for spatial-temporal analysis
Bibliographic record
Abstract
Reducing the road traffic injuries burden is relevant to many sustainable development goals (SDG), in particular SDG3 - to establish good health and well-being. To describe the spatial-temporal trends and identify hotspot regions for fatal road traffic injuries, a Bayesian hierarchical Poisson model was used to analyze data on vulnerable road users (bicyclist, motorcyclist and pedestrians) in Brazil from 1999 to 2016. During the study period, mortality rates for bicyclists remained almost unchanged (0.6 per 100,000 people) but rose dramatically for motorcyclists (from 1.0 in 1999 to 6.0 per 100,000 people in 2016) and decreased for pedestrians (from 6.3 to 3.0 per 100,000 people). Spatial analyses accounting for socio-economic factors showed that the central and northeastern microregions of Brazil are hotspot areas for fatal injuries among motorcyclists while the southern areas are for pedestrians.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".