Responsive upper limb and cognitive fatigue measures during light precision work: an 8-hour simulated micro-pipetting study
Bibliographic record
Abstract
Many contemporary occupations are characterised by long periods of low loads. These lower force levels, which are relevant to the development of work-related musculoskeletal disorders, are usually not the focus of fatigue studies. In studies that did measure fatigue in light manual or precision work, within and between measurement responses were inconsistent. The aim of this study was to identify fatigue measures that were responsive at lower force levels (<10% MVC) over the course of an 8-h period. A complementary set of fatigue measures, reflecting both neuromuscular and cognitive mechanisms, was measured during a light precision micro-pipetting task performed by 11 participants. Nine measures were found to be significantly responsive over the 8-h period, including: ratings of perceived fatigue, postural tremor, blink frequency and critical flicker fusion frequency threshold. Common field measures, specifically electromyography RMS amplitude and maximum voluntary contractions, did not lead to extraordinary time effects. Practitioner summary: The findings provide insight towards the responsiveness of a complementary set of field usable fatigue measures at low work intensities Although commonly used measures did not reveal significant increases in fatigue, nine alternative measures were significantly responsive over the 8-h period.
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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 | 0.001 | 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 | 0.001 | 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".