The Combined Effect of Cold and Moisture on Manual Performance
Bibliographic record
Abstract
Objective The aim of this study was to investigate the combined effect of cold and moisture on manual performance and tactile sensitivity. Background People working in the ocean environment often perform manual work in cold and wet conditions. Although the independent effects of cold and moisture on hand function are known, their combined effect has not been investigated. Method Participants completed sensory (Touch-Test, two-point discrimination) and motor (Purdue Pegboard, Grooved Pegboard, reef knot untying) tests in the following conditions: dry hand, wet hand, cold hand, and cold and wet hand. Results For the Purdue Pegboard and knot untying tasks, the greatest decrement in performance was observed in the cold-and-wet-hand condition, whereas the decrements seen in the cold-hand and wet-hand conditions were similar. In the Grooved Pegboard task, the performance decrements exhibited in the cold-and-wet-hand condition and the cold-hand condition were similar, whereas no decrement was observed in the wet-hand condition. Tactile sensitivity was reduced in the cold conditions for the Touch-Test but not the two-point discrimination test. CONCLUSION: The combined effect of cold and moisture led to the largest performance decrements except when intrinsic object properties helped with grasp maintenance. The independent effects of cold and moisture on manual performance were comparable. Application Tools and equipment for use in the cold ocean environment should be designed to minimize the effects of cold and moisture on manual performance by including object properties that enhance grasp maintenance and minimize the fine-dexterity requirements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".