Comparing the biomechanical and perceived exertion imposed on workers when using manual mechanical and powered cargo management systems during ladder loading and unloading tasks
Bibliographic record
Abstract
This study aimed to compare the effects of two different cargo management systems (one powered system, i.e., the RazerLift®, and one Traditional system) on the biomechanical and perceived exertion experienced by workers when performing routine ladder loading and unloading tasks. Seven experienced workers performed ladder loading and unloading tasks. Compared to when the Traditional system was being used, the cumulative static low-back compression force was reduced by 57.9% (p < 0.05) and 39.3% (p < 0.05) when the RazerLift® was being used to perform ladder loading and unloading tasks, respectively. Overall, the cumulative shoulder flexor moments were reduced by 34.8% (p < 0.05) and 41.3% (p < 0.05) when the RazerLift® was being used to perform ladder loading and unloading tasks, respectively. Overall, the perceived exertion score was reduced by 250.1% (p < 0.05) when the RazerLift® was being used compared to the Traditional system. In conclusion, our study demonstrated that the RazerLift® reduces the biomechanical and psychophysical exposure associated with the development of musculoskeletal disorders while performing ladder loading and unloading tasks.
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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 | 0.002 |
| 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) | 0.001 | 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".