1006 Comparisons between the differences in scanning patterns between novice and experienced load-haul-dump operators pre- and post- mining equipment simulator training
Bibliographic record
Abstract
<h3>Introduction</h3> Previous literature specific to simulator training in the mining industry has mostly been conducted by mining or simulator companies themselves, focusing solely on improvements to efficiency or procedures. There is a lack of evidence for how novice and experienced users perform on objective measures of workload including response times or eye movements. Eye fixations are found to be a useful measure of expertise and confidence along with an indicator of arousal or mental workload. The objective of this study is to determine differences in fixations between novice and expert load-haul-dump (LHD) operators, when completing training in a simulator. <h3>Methods</h3> Novice operators completing a four-day training program on an LHD simulator performed the same training run as an experienced operator. Tobii Pro Glasses 2 was used to collect eye movement data during first and last training runs. Particular emphasis was placed on manoeuvring and tramming, two work activities linked to fatal interactions with pedestrians. Scanning patterns of novice operators will be compared to expert using Tobii Pro Lab software. <h3>Results</h3> The projected results are that there will be noticeable changes in novice operators scanning pattern between their first and last training run, and will begin to resemble the expert operator’s eye behaviour over the period of training. Preliminary results demonstrate that the scanning patterns of the novice are more diverse and less focal than the expert. <h3>Conclusion</h3> Operating heavy machinery within dynamic environments of mines can be quite hazardous due to limited line of sight and confined spaces. Simulator training can minimise risk to operators and equipment, by allowing operators to gain skills in a controlled environment. These results will allow training facilities to recognise expert eye movement patterns and provide cues to novice users to rapidly improve their learning and ultimately lead to the prevention of accidents.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".