Research Using Virtual Reality: Mobile Machinery Safety in the 21st Century
Bibliographic record
Abstract
Whole-body vibration is a significant health risk for between 4% and 7% of the work force in North America. In addition, many factors compound the health risks of heavy machinery operators. For example, twisted trunk and neck postures stiffen the spine and increase the transmission of vibration to the head. Similarly, workers adopt awkward postures in order to gain appropriate lines of sight for machine operations. Although the relative contribution of these various issues can be evaluated in field studies and models, we propose that virtual reality is a powerful medium for investigating issues related to health and safety in mining machine operators. We have collected field data of posture and vibration, as well as visual environment, for a forklift operating in a warehouse. This paper describes the process and outcome of this field data collection, and provides a discussion on the next steps to develop and test the virtual reality model to enable laboratory testing. Our ongoing studies will evaluate the interplay between posture and vibration under conditions replicating routine heavy machinery operations, such as underground mining.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".