FATIGUE ANALYSIS OF HIGH DUMP TRUCK OPERATORS IN INDONESIA’S COAL MINING INDUSTRY: A CASE STUDY
Bibliographic record
Abstract
A coal mining industry typically applies a 24-hours working time, which enforces some workers to stay conscious during night shift, opposing human body's biological clock. This study aims to analyse the level of fatigue experienced by high dump truck operators (HD operators) in a coal mining site in East Kalimantan, Indonesia. This study utilizes primary data which obtained from distributing Industrial Fatigue Research Committee (IFRC) survey to all HD operators and secondary data (for Fatigue Likelihood Scoring - FLS) which consists of HD operators’ working schedule that currently applied in the company. Results obtained is analyzed using Fatigue Risk Management System (FRMS) framework which combines FLS classification and Dawson-McCulloch’s model of fatigue risk trajectory. This study reveals that based on IFRC survey, HD operators experienced low/mild fatigue due to insignificant influence of fatigue-related factors contained in the survey. However, consideration for improvement is in need since the result of fatigue for night shift operators is close to moderate level. In addition, based on FLS, the level of fatigue indicates that HD operators experienced excessive working hours, in which in FRMS graph classified as fatigue-related errors. Thus, this study proposes several strategies as the hazard control mechanism: (1) providing optimum resting time, (2) equipping operators with audio music that lead to positive energy and increasing work focus, and (3) adding afternoon shift to balance the working hours.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".