Design of Worker Rotation for a Precast Concrete Pole Factory Based on Mental Workload
Bibliographic record
Abstract
This study examines the effect of temperature and noise on workers' mental workload and evaluate the effectiveness of job rotation in mitigating these impacts.The manufacturing plant has a temperature of 35.86 degrees Celsius from the pile formation process and a noise level of 89.32 dBA from the process of releasing piles from the mould.High temperatures and noisy work environments cause workers to feel stressed and fatigued, leading to longer task completion times.The factory implements 2 work shifts per day, but if the daily production target is not met, there will be additional work shifts.Who will work in this irregular addition of work shift is also unclear.The extra shift workers come from the second shift workers of that day and the first shift workers the following day.Additional working hours increase the mental workload for the workers.This problem can be solved through job rotation.This research is classified as explanatory research with the object of study being the work environment and workers of the pile manufacturing factory.The mental workload is measured using the subjective workload assessment technique (SWAT).The novelty of this research lies in the design of work rotation based on mental workload, where in 2 shifts, workers rotate every four hours.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".