Application of REBA and QEC Methods in Redesigning Clamping Workstations to Enhance Ergonomic Performance
Bibliographic record
Abstract
This study focuses on redesigning the work platform height for workpiece tightening activities at PT. Xyz, utilizing the Rapid Entire Body Assessment (REBA) and Quick Exposure Check (QEC) methods to assess the risk level of the activity.The results from REBA indicated a high-risk score of 10, while the QEC showed a 61% exposure score, confirming the need for immediate improvements.Based on the analysis of anthropometric data from workers at the CNC FH8800 workstation, the optimal work platform height was determined to be 64.3 cm, whereas the current platform height is 120 cm.A reduction of 55.7 cm is necessary to achieve an ergonomic design, which is expected to reduce operator fatigue and increase productivity.This study demonstrates how ergonomic adjustments based on anthropometric data can improve work conditions and operational efficiency.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".