Using visibility tools in Classic JACK to assess line-of-sight issues associated with the operation of mobile equipment
Why this work is in the frame
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Bibliographic record
Abstract
Several methods exist for measuring line-of-sight (LOS) associated with the operation of operator controlled mobile equipment but there is no consistent criteria used for all machine types or operating scenarios. Moreover, the standards that do exist are not particularly applicable for carrying out LOS analysis on mobile mining equipment. This paper describes a new method to evaluate LOS associated with the operation of mobile equipment, the LOS boxplot. The human simulation program, Classic JACK was used to analyse LOS from the operating position of five different load-haul-dump (LHD) vehicles, typically used in underground hardrock mining. The LOS boxplot method was able to demonstrate LOS associated with five LHD vehicles, and it was able to illustrate LOS improvements associated with the design modifications tested. The examples provided also show the applicability of the method for evaluating operator LOS from other types of heavy equipment.
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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 it