Analysing human effect on the reliability of mining equipment
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
In the mineral industries, the heavy equipment is widely used, and the operator performance is a significant factor for fulfilling intended functions of this equipment. This study aims to analyse and interpret the effect of the human factor on the reliability of the equipment used in the mining industry. In doing so, a combined approach based on the reliability analysis, statistical inference and a machine learning technique is proposed. A case study was conducted on haul trucks in a mining operation. The case study showed that the reliability drops of haul trucks vary in the range of 0.84% and 2.45% in a shift. It was also calculated that 16.9% of these reliability drops were associated with operator habits. A truck's condition at the beginning of a shift and the initial reliability contribute to 36.3% and 31.1% of the reliability drops, respectively. The findings can be used in scheduling training programs, short-term mine planning, and simulation of material handling systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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