Sun Glare: Network Characterization and Safety Effects
Bibliographic record
Abstract
This research conducted a two-stage assessment to investigate sun glare effects on road safety in Edmonton. The first stage developed a methodology to model sun glare occurrence, aiming to identify when and where drivers are most likely to be exposed to sun glare. Safety risks at those identified locations during sun glare periods were assessed in the second stage. By contrasting the collisions during glare and non-glare conditions, this second stage aimed to quantify the effects of sun glare on road collisions. Consequently, three major findings were concluded. First, sun glare was found to significantly contribute to collision occurrence, especially at road intersections. Second, the effects of sun glare on collision occurrence during mornings on the eastbound and evenings on the westbound were observed to be particularly worse in the spring and fall months. In fact, safety in the southbound direction was significantly affected by sun glare during most daytime hours in the main winter months (November, December, and January). Lastly, the analysis revealed that certain collision types were more likely to occur during periods of sun glare. For example, collisions due to signal violations and failing to yield to pedestrians/cyclist were more likely to occur at intersections. At mid-block locations, the proportion of collisions occurring due to improper turning and lane changes were observed to be significantly higher. Overall, the approach proposed in this research provides a holistic method to quantify the effects of sun glare on road collisions and offers additional insights into the extent by which sun glare affects road safety.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".