Interpreting Workload Variation Using Fatigue-Recovery Modeling
Bibliographic record
Abstract
OCCUPATIONAL APPLICATIONSThis viewpoint article addresses an approach to understanding the impact of physical workload variation using fatigue-recovery type models. Seven examples are presented in which fatigue-recovery models, including a range of fatigue types, are used to interpret the effects of time-series workload patterns without necessarily quantifying workload variation directly. These examples of fatigue-recovery model analysis approaches have been risk-validated to MSDs, validated against worker’s subjective performance, and linked to manufacturing quality deficit outcomes. While these fatigue-recovery modeling approaches aimed to understand the effects of variable workload show promise, a number of challenges remain before they can be more widely deployed in practice. This includes the need for better underlying models using data from a broader range of participants, and the application supports needed to use the approach proactively in work system design. The authors argue that resulting ‘fatigue’ indicators can be more easily understood, and therefore more readily used and more meaningful in decision making, than more complex biomechanical variables currently used in occupational workload studies.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".