The effect of platform motions upon the biomechanical demands of lifting tasks
Bibliographic record
Abstract
The purpose of this study was to examine the biomechanical demands associated with MMH performed in moving environments. Twelve healthy male subjects performed four different lifting tasks (referred to as 10U, 15U, Close25 and Far25) while exposed to a simulated ship motion profile. Dependent measures included electromyographic (EMG) signals from several trunk muscles and thoracolumbar motions collected via a Lumbar Motion Monitor (LMM). A repeated measures ANOVA was employed to examine the differences between thoracolumbar velocities and trunk EMG activities between successful lifts and lifts during which a motion induced interruption (MII) was identified. The maximum EMG signals increased as MII events occurred for the left and right erector spinae and external obliques. The 10U lifting task significantly differed from both the Close25 and Far25 lifting tasks in the maximum left trapezius and the 10U lifting task differed from all other lifting tasks for the maximum right trapezius activities. There were increases in the maximum thoracolumbar velocities in the lateral bending and twisting planes for lifts incurring a MII across all lifting conditions when comparing successful lifts. These data suggest that performing tasks in moving environments will place an operator at an increased risk for musculoskeletal injuries, particularly when the rate of MII is high.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".