Efficacité du pré-signalement d'arrêt scolaire avec feux jaunes et avec feux de détresse
Bibliographic record
Abstract
The pre-stop warning is activated by the bus drivers to warn motorists that the school bus will soon stop, requiring all vehicles to stop. On buses equipped with an eight-light system, four yellow flashing lights, located near the roof, precede the four red flashing lights activated with the stop-arm. In Québec, where pre-stop warning is not required, it is permitted to use the "hazard lights" as a pre-stop signal, when the school bus has red lights only. This study rates the relative effectiveness of the two systems, in fall and spring time. Advance signal lights are tested on the same routes : two- and four-lane rural or near urban highways, with high posted speeds (70 km/h and over). A video camera is aimed at oncoming traffic along with a radar antenna. Changes in drivers' speeds are studied with a relative risk ratio and an efficiency index, validated through expected frequencies. The eight-light system reduced significantly the rate of illegal passing and the overall speed during advance signal. The eight-light system was more effective than the hazard lights for all tested parameters, including visibility, traffic, weather, and season. The near-roof position of the yellow lights probably explains the gap between the two systems.Key words: advance signalling device, pre-stop warning, eight-light system, amber lights, hazard lights, school buses, illegal passing, speed, relative risk ratio, road safety, rural area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".