Influence of surgical masks on the avoidance of virtual pedestrians
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To combat the COVID-19 pandemic, governments around the world have proposed a series of mitigation strategies. While responses varied across different governing bodies, recommendations such as social distancing and the use of surgical masks were nearly universal. These recommendations, as well as the social anxiety that emerged during the pandemic, are likely to have influenced pedestrian interactions. In this study, we have examined the effect of surgical masks on locomotor circumvention strategies in response to virtual pedestrians. We further explored the relationship between measures of obstacle clearance and feelings of anxiety related to community ambulation in the context of the pandemic. Using virtual reality, locomotor circumvention strategies in response to approaching pedestrians with and without surgical masks were measured in a sample of 11 healthy young individuals. Additionally, a questionnaire was developed and used to gain insights into participants’ behaviours during and after a strict period of restrictions that were in effect before the summer of 2020. Results showed that participants maintained a larger clearance when virtual pedestrians wore a surgical mask. Furthermore, clearance was positively associated with anxiety toward community ambulation in the context of the pandemic. Our findings provide evidence that mask-wearing elicits an increase in physical distancing during pedestrian interactions. Furthermore, results indicate that social context and mental health status influence locomotor outcomes measured in the context of a pedestrian interaction task and highlight the potential of virtual reality simulations to study locomotion in the community setting.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it