Task‐dependent differences in subjective fatigue scores
Bibliographic record
Abstract
The aim of the present study was to evaluate time-on-task effects on subjective fatigue in two different tasks of varying monotony during night-time testing (20:00 to 4:00 hours) in a sleep deprivation intervention. The experiment included eight test runs separated by breaks of approximately 20 min. Twenty healthy volunteers performed a driving simulator and the Mackworth clock vigilance task in four of the test runs each. Sequence of tasks was varied across subjects. Before and after each task, subjective sleepiness was assessed by means of the Karolinska sleepiness scale and subjective fatigue was rated on the Samn-Perelli checklist. Fatigue and sleepiness significantly increased over the course of the night. Both tasks led to an increase in fatigue and sleepiness across test runs. However, this time-on-task effect was larger in the vigilance than in the driving simulator task. It is important to note that fatigue and sleepiness in one test run were not influenced by the task performed in the preceding test run, that is there were no cross-over effects. The results suggest that time-on-task effects superimpose circadian and sleep-related factors affecting fatigue. They depend on the monotony of the task and can be quantified by means of a design including separate test runs divided by breaks.
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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 | 0.000 | 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".