Estimating maximum and psychophysically acceptable hand forces using a biomechanical weakest link approach
Bibliographic record
Abstract
Accurate estimation of occupational performance capability facilitates better job (re-) design by informing workplace parties about the potential mismatches between job demands and the capability of their labour force. However, estimating occupational performance requires consideration of multiple factors that may govern capacity. In this paper, a novel model is described that uses a stochastic algorithm to estimate how variability in underlying biomechanical constraints affects hand force capability. In addition, the model estimates psychophysically acceptable hand force capacity thresholds by applying a biomechanical weakest link approach. Model estimates were tested against experimentally determined maximal and psychophysically determined hand forces in two exertion directions in constrained postures. The model underestimated maximum hand force capacity relative to measured maximum hand force by 30% and 35% during downward pressing and horizontal pulling, respectively. These values are consistent with those observed using previous two-dimensional models. Psychophysically acceptable hand forces were also underestimated by 29% during both pressing and pulling. Since the psychophysical estimates were scaled as a percentage of the estimated maximum capacity, this suggests that the underestimation in both predictions may be corrected by improving estimates of maximum hand force. Psychophysically acceptable forces were observed to be partially governed by demands at the biomechanical weakest link.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".