Bio-mechanical Analysis on the Lower Back using Human Model during Pushing the Manual Vehicles
Bibliographic record
Abstract
A high prevalence of protected horticulture farmer's work-related musculo-skeletal disorders (MSDs) have been reported in precedent studies. One of the tasks required ergonomic intervention to reduce the musculo-skeletal risks is the task of product transporting. The purpose of this study is to evaluate quantitatively the spinal load of operator using manual vehicles to predict and prevent musculo-skeletal risks. Spinal load in operators using 4 kinds of manual vehicle were analyzed. Before evaluating spinal load on operator using the manual vehicles by bio-mechanical approach, it is needed to validate human model. In this study, ADAMS LifeMOD human model shows satisfactory results, comparing with already validated model's results or measured results. While Operators pushed the manual vehicles(wheelbarrow, Trolley, 2 wheel cart, and 4 wheel cart) contained loads that were 0 N and 800 N, their spinal loads(compression force, shear force) were evaluated. The compression force demonstrated under the NIOSH action limits - 3410N - for all 4 manual vehicle's operators(McGill 1997; Marras 2000). However, the lateral shear force demonstrated over the University of Waterloo - 500N - for all 3 manual vehicle's operators except 4Wheel cart (Yingline and McGill, 1999). Therefore, operators have risks in prevalence of the musculo-skeletal disorders due to shear force. The findings of this study suggest that it need to be determine the spinal load, especially lateral shear force in designing the manual vehicles in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".