Effect of simulated vessel motions on thoracolumbar and centre of pressure kinematics
Bibliographic record
Abstract
The purpose of this study was to examine the effect of simulated vessel motions and load stabilities on thoracolumbar kinematics and foot centre of pressures (CoP) kinematics during the execution of common manual material handling (MMH) tasks. Nine male subjects completed a standing, holding and lifting task in three different simulator motion conditions (no motion, pitch and roll) while handling both a stable and unstable load (15 kg). Stability of load did not affect thoracolumbar kinematics, CoP or occurrence of motion induced interruptions (MIIs) while lifting or holding a load in any of the motion environments. Although no differences in thoracolumbar kinematics and CoP during the dynamic lifting task between baseline and either pitch or roll conditions were found, total anterior-posterior CoP displacements and thoracolumbar velocities were greater during motion conditions for stationary holding and standing tasks. MIIs occurred more often during the standing task than the lifting tasks. These results suggest that the rate of MII occurrence may be a function of the dynamic nature of the MMH task being performed. Tasks which restrain the movement of the individual may prevent the body from making adequate adaptations when stability is disturbed, increasing the likelihood for change-in-support strategies, potential for loss of balance and resultant injury.
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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) | 0.000 | 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".