How Much Practice Is Required to Reduce Performance Variability in a Virtual Reality Mining Simulator?
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
Virtual reality allows researchers to explore training scenarios that are not feasible or are potentially risky to recreate in the real world. The aim of this research was to examine whether using a tutorial session prior to using the mining simulator could adequately reduce the performance variability and increase the consistency of participant performance metrics. Eighteen participants were randomly assigned to a tutorial or a non-tutorial group. The tutorial group completed a five-minute tutorial that introduced them to the basics of the machine and virtual reality environment. All participants then completed five sessions in the simulator lasting five minutes each. Personality scores were recorded and participants answered questions to test their situational awareness after each session. Performance metrics such as number of collisions and perception response time were recorded by the simulator. A Wilcoxon signed rank test was used to determine at what point a significant difference in performance metrics was apparent across the five sessions. A mixed effects multilevel regression was done to evaluate the change in variability across time. There were no significant correlations between the personality questionnaire scores and the number of collisions or the perception response time. Both groups demonstrated high standard deviation scores for collisions and perception response time, but the tutorial group had decreasing variability across time. Both groups began to exhibit more consistent scores in the simulator after 10 min of use. Situational awareness questions require some refinement prior to further testing.
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.
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 | 0.004 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it