Comparison of operator line-of-sight (LOS) assessment techniques : evaluation of an underground load-haul-dump (LHD) mobile mining vehicle
Bibliographic record
Abstract
For many years line-of-sight (LOS) issues for underground mobile equipment is a growing focus of research. This research is a result of the numerous fatalities and injuries which occur in the mining industry and which are related to poor operator LOS. Three assessment methods are currently used to assess operator LOS of underground mobile equipment. The light filament (LF) method is a hands-on assessment method that is performed in the field. This method is not evaluated in this paper. The laser scan (LS) method of assessing equipment is a quick and reliable method that comes from the need to evaluate vehicles already located in the field. The computer simulation (CS) method can assess LOS issues using a computer aided drawing (CAD) model, which is useful for assessing current or prototype models. The purpose of the current research is to compare and validate different operator LOS assessment methods. Comparison of the results of the LS and CS visibility plots yielded similar quantifiable results. A visual comparison of the results further illustrate that the LS and CS methods are acceptable assessment tools for mobile equipment operator LOS evaluation. The visibility assessment methods are now being prepared as guidelines for the mining industry to assess current and potential designs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".