Estimating Haul Truck Dutymeters Using Operational Data
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
Modern mobile mining equipment is becoming increasingly more instrumented in an effort to reduce operating costs through production and operation monitoring. This provides access to large amounts of data, primarily related to machine operation, not previously available. This work examines applicable operational loading parameters, and their relationship to haul truck component reliability. The Cox proportional hazards model was used to determine which parameters were strongly related to hoist cylinder and final drive reliability. These “dutymeters” can then be used to optimize particular aspects of the maintenance program, specifically to indicate the feasibility of component utilization beyond current replacement benchmarks. Components that have been lightly loaded throughout their operational life, as indicated by the defined dutymeters, could qualify for extended use with a greater chance of reliability.
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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 | 0.000 | 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