Postural implications of obtaining line–of–sight for seated operators of underground mining load-haul-dump vehicles
Bibliographic record
Abstract
Operators of load-haul-dump (LHD) vehicles use awkward postures that may be held statically and at extreme ranges of motion for long shift periods to spot hazards in underground mining. This study examined postural variables associated with three amounts of seat rotation intended to maximize line-of-sight during forward driving. Three different models, representing the 1st, 50th and 99th percentile male for height and weight, were positioned with appropriate hand and foot constraints in the virtual LHD cab modelled in Classic JACK v4.0. A total of 15 virtual movement strategies were developed to model the postural behaviour of typical workers and each virtual subject was tested, first with the seat in a neutral 0 degrees position and then with it rotated counter-clockwise to 20 degrees and 45 degrees . Results revealed that reductions in trunk rotation, trunk lateral bend and neck rotation were associated with the seat rotation intervention. The general relationship observed was that as seat rotation increased, view of critical visual attention locations and visible line-of-sight area increased while postural load variables decreased. For the most part, 20 degres of seat rotation was beneficial but 45 dgrees produced significantly greater changes to postural load and visible visual attention locations.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".