Transportation Association of Canada
Bibliographic record
Abstract
Over the past five years, the City of Edmonton has averaged approximately 21,000 total collisions annually, resulting in an average of approximately 6,800 injury collisions and $74,000,000 in property damage annually. Approximately 60 percent of these collisions have occurred at intersections, accounting for 65 percent of the total injuries and 40 percent of the total fatalities reported. Through engineering, education, enforcement and evaluation, the City of Edmonton and Edmonton Police Service are working to reduce the number of collisions and injuries, which are resulting from driver behaviour and related human factors. Edmonton has been an active partner of the Capital Region Intersection Safety Partnership (CRISP) since its development in 2000. The mandate of CRISP is to build awareness for traffic safety issues at intersections. The group has successfully implemented public awareness campaigns addressing red light running, pedestrian safety and speeding through intersections. The Edmonton Red Light Camera Program was launched in 1999 and will ultimately operate with 20 cameras and 60 locations by the end of 2004. Existing locations have shown an
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.514 | 0.301 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".