Goma Road security Determinants in the Democratic Republic of Congo: Report analysis from Police oral trials
Bibliographic record
Abstract
Introduction: Road traffic accidents constitute a major public health problem because of death, disability and trauma with medical, surgical, psychological, mental, economic, social and sometimes legal formidable complications resulting from them. Socio-professional reintegration of the survivors of accidents can become complex. This study identifies the main determinants of road security in Goma in the Democratic Republic of Congo and offers prevention strategies adapted to the context. Methodology: The study is descriptive cross and analysis data collected from police oral trials about traffic accidents occurred during 2015. Resultats: The study essentially shows that 36% of the accidents occurred on weekends (Saturday and Sunday); 25.5% of the accidents took place between 18 and 21 hours; the main cause of accidents was the bad driver behavior, including speeding and drunk steering wheel. Serious injuries (24.5%) and death (11.9%) were dreadful consequences. Discussion and conclusion: Accidents can be avoided. The study proposes strategies to reduce road traffic accidents by securing users the road, the vehicle and the road infrastructure. The implementation of these strategies is heavily dependent on the political will of the authorities of the DR Congo.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".