Development of a new operator visibility assessment technique for mobile equipment
Bibliographic record
Abstract
Over the last three decades, the mining industry has been moving towards underground mechanized mining methods and the number of load haul dump (LHD) vehicles utilized has increased. The growth of mechanization and automation has benefited both workers and mining companies. However, due to the design constraints with LHD vehicles and the limitations of the operating environment, restrictions to operator visibility has contributed to a number of accidents, including fatal injuries. Past researchers have used the light filament technique to collect obscuration zones around mobile equipment and have produced 2D visibility charts (shadow diagrams) of this information. The light filament method involves manually collecting visibility data, which results in errors and is time consuming, requiring around three hours to complete. This research utilizes a MENSI GS100 laser scanner along with 3D modelling software (3Dipsos) to collect and process operator visibility profiles for underground mobile equipment. Results from this test work indicate that a laser scanner can be successfully used to rapidly collect this data and utilize this information for improved mobile equipment design from a visibility perspective. Nomenclature: FERIC Forest Engineering Research Institute of Canada ISO International Organization for Standardization JACK Software for digital human modelling and ergonomics LHD Load haul dump vehicle LOS Line of sight MASHA Mines and Aggregates Safety and Health Association (based in Ontario, Canada) PVD Polar visibility diagram SAMMIE 3D human modelling computer aided ergonomics design system WSIB Workplace Safety and Insurance Board
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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 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".