Platform simulated wave motions inhibit neuromuscular responses
Bibliographic record
Abstract
Background: Seafaring workers must contend with motions that could impact their work performance and safety. Objective: To compare and analyze the neuromuscular responses to a stable immediate environment placed in a moving (simulated wave platform motions) extended environment. Methods: Isometric maximum voluntary contraction (MVC) forces of the elbow flexors and leg extensors as well as electromyography (EMG) of the biceps brachii and vastus lateralis were recorded. The EMG activity of the triceps brachii, semitendonosis, internal obliques, and lower lumbar erector spinae muscles were also monitored during the upper and lower limb MVCs. Three types of rotational motion (pitch, roll and mixed, all combined with a linear heave motion) created by a motion platform were randomly allocated for 1 minute each. While securely strapped and seated with the platform moving, two MVCs each were performed for the right elbow flexors and right knee extensors at the beginning and at the end of the one-minute wave motion protocol. Results: Platform motions impaired (p<0.0001) knee extension (pitch= −8%; roll=−13.4%; mixed=−13.5%) and elbow flexor MVC force (pitch=−21.1%; roll=−26.7%; mixed=−25.1%) compared to control conditions. Vastus lateralis EMG was reduced 13.3% with roll motions compared to control. Conclusions: Platform simulated wave motions can be detrimental to force production when the individual is strapped in a secure and stable seated position. Such impairments can impact the safety and work performance of employees on sea going vessels.
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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 | 0.001 |
| 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) | 0.005 | 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".