A preliminary study of tramming speeds in multiple tele-operated load-haul-dump scenarios using QUEST®
Bibliographic record
Abstract
Tele-operated Load Haul Dump (LHDs) machines are becoming a common tramming solution throughout many mines at INCO Limited in Copper Cliff, Ontario, Canada. To reach maximum productivity from multiple tele-operated LHDs, the system must achive a proper balance of LHD speed in the haulage network and haulage layout geometry. A study was inititated to determine if multiple LHDs, tramming in second gear under automatic guidance, would influence the total throughput of a production process. A simulation model was used to evaluate the haulage system throughput with the LHDs tramming in second gear while under automatic guidance. The study indicated that allowing LHDs to operate in second gear for the specificed haulage layout configuration, may not provide an increase in the system capacity. The paper concludes that further investigation of key tramming system variables should be carried out to optimize LHD speed with the haulage layout geometry.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".