Bibliographic record
Abstract
OBJECTIVE: The objective was to develop an equation, for repetitive tasks, that uses frequency and/or duty cycle (DC) to predict maximum acceptable efforts (MAE) relative to maximum voluntary efforts (MVE). BACKGROUND: Ergonomists must determine acceptable physical demands for a wide variety of tasks. Although a large database exists in the literature for maximum single-effort strength, far fewer repetitive tasks have psychophysical and/or physiological data available to guide the prediction of acceptable submaximal, repeated efforts. METHOD: DC represents the total effort duration divided by the cycle time. MAEs were calculated by dividing average psychophysics-based acceptable loads by corresponding single-effort maximum strength using 69 values from studies of the upper extremities. The author developed an equation to characterize the relationship between MAE and DC. RESULTS: The resulting equation had DC taken to the exponent 0.24, and it predicted MAE very well (r2 = 0.87%, root mean square [RMS] difference = 7.2% of the maximum strength). At higher DC values, the equation also demonstrated good agreement with the published physiological data. CONCLUSION: The limited psychophysical database in the literature makes it difficult for ergonomists and engineers to recommend acceptable efforts for the large variety of repetitive tasks they evaluate. However, the proposed equation now allows for a correction of the large strength database to estimate acceptable force and torque limits for repetitive occupational tasks. APPLICATION: The proposed equation will have wide applications for ergonomic practitioners performing evaluations of repetitive 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".