Analysis of the Interaction between Human Operator and Automated Dispatch in Haul Truck Scheduling
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
This paper presents the findings from a field study of human-automation interaction in an open pit gold mine.Motivated by an earlier study that identified problematic interaction between haul truck operators and dispatch interfaces, focus groups and questionnaires were used to understand what causes the general attitude of suspicion towards the system.Overall trust in the dispatch interface, as well as usability and functionality of the system, were judged as slightly positive.However, the inability of the system to react efficiently to sudden changes on site results in operator frustration.We argue that consequently, the human operator should be utilized as a sensor by the dispatch system.Through operator involvement in the stage of information acquisition, the system's response to sudden changes can be improved, and discontent reduced.
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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