A psychophysical study to determine maximum acceptable efforts for a thumb abduction task with high duty cycles
Bibliographic record
Abstract
Potvin (2012, 'Predicting Maximum Acceptable Efforts for Repetitive Tasks: An Equation Based on Duty Cycle', Human Factors: The Journal of the Human Factors and Ergonomics Society, 54 (2), 175-188) developed an equation using psychophysical data to estimate maximum acceptable efforts (MAEs) as a function of duty cycle (DC). However, only ∼6% of the data featured DCs ≥ 0.50. The purpose of this study was to evaluate the MAE equation in the high DC range. We tested a repetitive thumb adduction task with DCs of 0.50, 0.70 and 0.90, at frequencies of both 2 and 6 per minute (n = 6 conditions). Participants were trained for 2 hours and tested for 1 hour on each condition. The MAE decreased with increasing DC, and MAEs at 2/min were higher than those at 6/min. When these current six means were added to the original psychophysical studies, the root-mean squared difference of the MAE equation decreased from 7.23% to 7.05% maximum voluntary contraction. The values from our study are also consistent with those demonstrating physiological evidence of fatigue during both continuous isotonic and high DC tasks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".