Using a Virtual Environment to Examine How Children Cross Streets: Advancing Our Understanding of How Injury Risk Arises
Bibliographic record
Abstract
PURPOSE: To examine how risk of injury can arise for child pedestrians. METHODS: Using a highly immersive virtual reality system interfaced with a 3-D movement measurement system, younger (M = 8 years) and older (M = 10 years) children's crossing behaviors were measured under conditions that introduced variation in vehicle speed, distance, and intervehicle gaps. RESULTS: Children used distance cues in deciding when to cross; there were no age or sex differences. This increased risk of injury in larger intervehicle gaps because they started late and did not monitor traffic or adjust walking speed as they crossed. In contrast, injury risk in smaller intervehicle gaps of equal risk (i.e., same time to contact) occurred because crossing behavioral adjustments (starting early, increasing walking speed while crossing) were not sufficient. CONCLUSIONS: Dependence on distance cues increases children's risk of injury as pedestrians when crossing in a variety of traffic situations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".