Simulated motion negatively affects motor task but not neuromuscular performance
Bibliographic record
Abstract
The effects of long duration simulated motion on motor task and neuromuscular performance along with time frames required to recover from these effects are relatively unknown. This study aimed to determine (1) how simulated motion affects motor task and neuromuscular performance over one hour of motion and (2) the time course of recovery from any decrements. The dependent variables that were measured included: reaction time; visuomotor accuracy tracking; maximal voluntary contractions; voluntary activation; evoked contractile properties and biceps brachii electromyography of the elbow flexors. Reaction times and error rates of the visuomotor accuracy tracking task were compromised in motion, but maximal force, voluntary activation, evoked contractile properties and rmsEMG responses of the biceps brachii were unaffected by motion. It is concluded that motion causes an increase in attention demands, which have a greater effect on motor task rather than neuromuscular performance. PRACTITIONER SUMMARY: Minor delays or mistakes can separate life and death at sea. The safety and productivity of most vessels rely on error-free performance of motor tasks. This study demonstrates that human ability to perform motor tasks is compromised by ship motions and may aid in developing training and safety guidelines for seafarers.
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 | 0.002 |
| 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) | 0.002 | 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".