Use of Virtual Reality to Increase Awareness of Line-of-Sight Hazards around Industrial Equipment
Bibliographic record
Abstract
Impaired operator line of sight has been implicated in several pedestrian–equipment accidents and fatalities in the mining industry. Existing training methods for conveying visibility information lack worker engagement and may be insufficient to capture the dynamic, three-dimensional nature of blind spots around industrial equipment. The present study utilized a custom virtual reality experience intended to shift the way in which visibility information is presented. Visibility knowledge, confidence levels and safe pedestrian behaviors around the load-haul-dump vehicle were examined among participants in control and experimental (virtual reality and conventional training) groups (n = 72). Results demonstrate that the virtual reality intervention was not effective for increasing visibility knowledge and safe pedestrian behaviors relative to controls, although the performances of the virtual reality and conventional training groups were comparable. A discrepancy was identified in the perceived versus actual visibility knowledge and safe pedestrian behaviors at the rear of the load-haul-dump vehicle among the virtual reality training group. The findings suggest poor knowledge transfer between the three-dimensional virtual reality experience and the two-dimensional visibility plot used. The work also speaks to the importance of emphasizing rear-facing visibility deficits around machinery within industry safety training materials.
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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".