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 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 | 0.006 |
| 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) | 0.001 | 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".