Injury Risk in Collisions Involving Buses in Alberta, Canada
Bibliographic record
Abstract
With 2000 to 2007 crash data, this study investigated the factors that contributed to injuries in collisions that involved at least one bus in the province of Alberta, Canada. Crashes of all types of buses (e.g., school, transit, intercity) were considered. Four logistic regression models were calibrated: single-vehicle collisions on highways, single-vehicle collisions on nonhighway locations, two-vehicle collisions on highways, and two-vehicle collisions on nonhighway locations. The analysis showed that weather conditions were a significant contributing factor in all four types of collisions, although crashes in adverse weather conditions resulted in fewer injuries. The type of collision, characteristics of collision partner, driver age of collision partner, and weather conditions had a significant effect on the level of severity of collisions on both highway and nonhighway locations. Other factors were shown to affect injury risk only in one particular situation. For instance, for highway-related collisions, the age of the collision partner had a significant effect on levels of accident severity, whereas the age of the bus driver did not. In addition, for highway collisions, the severity was higher for head-on crashes, bus–bus crashes, bus–truck crashes, bus–motorcycle crashes, older buses, crashes on grade and in sags, and crashes during dark and sun glare, whereas accident probability decreased with larger outside shoulder width. For nonhighway locations, crashes occurring near tunnels, overpasses, and signalized intersections were shown to result in a higher probability of injury. The results showed that single-bus collisions involving pedestrians at nonhighway locations had higher injury risk than collisions involving objects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".