A framework for realistic microscopic modelling of surface mining transportation systems
Bibliographic record
Abstract
This article presents a recently developed realistic microscopic simulator for surface mining transportation systems called Surface Mining Transportation Simulator (SuMiTSim). In the road transport sector recent researches proved that for proper deployment of Intelligent Transportation Systems (ITS), the use of microscopic simulation models rather than the conventional macroscopic ones is critical. Microscopic simulators emulate realistically the dynamic traffic on a road network. A conceptual framework for the development of a surface mining micro-simulator is then proposed. The implementation of this framework led to SuMiTSim which is a robust tool for truck traffic analysis and control. Two case studies have been conducted. The results obtained from the first case study show clearly the benefits that can be derived when using SuMiTSim as a laboratory for more efficient haul roads design. The second finding concerns the integration of SuMiTSim as a proactive updater for real-time allocation. Other potential uses of SuMiTSim are highlighted, such as for sound environmental management through controlling fuel consumption and reducing truck bunching effects on mine networks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".