Ergonomic Working Design Model in Reducing Fatigue due to Air Traffic Control (ATC) at Kuala Namu Airport, Indonesia
Bibliographic record
Abstract
Generally, humans can work properly and achieve optimal results when supported by good environmental conditions. The effect associated with the inconvenience of the working environment can be experienced over a prolonged period. Therefore, this study aims to determine the ergonomic working design model in reducing fatigue due to Air Traffic Control (ATC) at Kuala Namu Airport, Deli Serdang – Medan, Indonesia. This is because it is necessary to consider the physical condition or health conditions of controllers to enable the company to extend its services' life and profitability. Rapid Upper Limb Assessment (RULA) is the research method used to investigate upper limb disorders. RULA was developed as a method to detect posture, which is a risk factor. This method is designed to assess workers and determine the musculoskeletal loads likely to disrupt upper limbs. Under such conditions, the management is advised to develop 'open management' based on political will, which involves operators in every step of the improvement, because they have adequate ideas of the problems at hand. Participation in ergonomics enables employees with their supervisors and managers to apply adequate knowledge in their workplace to enhance working environment conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".