MATCHING MACHINE DESIGN TO THE PRODUCTION PROCESS: A CASE STUDY IN THE INTEGRATED DESIGN OF MOBILE EQUIPMENT AND MINING METHODS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Underground mining imposes very rigid constraints on mobile equipment design. The choice of a particular mining “method” – i.e. the specific mix of techniques for excavation, ground support, and materials handling – is greatly influenced by the nature of the ore body being exploited. Mining methods tend to be fairly conservative, relying upon well established and proven equipment designs. In order to improve worker safety and productivity, South African platinum mines have increasingly turned to mechanization. An added benefit of these mechanization efforts is that the nature of the mining can be modified based on the feasible equipment designs. These efforts have resulted in changes to the mining methods employed in South Africa’s narrow-reef platinum group metal (PGM) ore bodies, as well as the development of a suite of mobile equipment which enables implementation of the new production processes. This paper focuses on the design and development of one of these machines - a narrow-reef bulldozer suited to selective mining. The resulting machine is a miniature unmanned bulldozer and multipurpose crawler platform designed for narrow-vein mining applications, with integrated mechatronics and remote control capabilities. This paper will discuss the development of the machine and the applications for which it was designed.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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