A database development and analysis of selected side impact collisions in Toronto
Bibliographic record
Abstract
This thesis is based on the initial phase of a project that developed an in-depth collision database and performed an analysis of police reported side-impact collisions for the City of Toronto intersections between 1998 and 2000. Currently, collision data exists through several different sources in Ontario. The development of a database involving the amalgamation of collision forms, the selection of data fields, and the collection of real collision data from selected, thoroughly investigated side impact collisions involving late model vehicles (1998 and newer), is described. For analysis, Statistical Analysis Software Release 8.02 was used to investigate causation and causal factors of side impact collisions. Statistically significant collision factors determined by fault propensity included apparent driver action, driver age, front seat passenger age, maximum posted speed, approximate vehicle speed, road character, and number of lanes. For intersection collision propensity, statistically significant findings included the system used, presence of flashing signals, intersection legs, roadway volume, and intersection leg road classifications. It is anticipated that the findings from this analysis can provide insight into significant factors in side-impact collisions that will be applied with greater focus to the in-depth collision database, once developed. Traffic accidents
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".