Evaluation of Protective Gloves and Working Techniques for Reducing Hand‐arm Vibration Exposure in the Workplace
Bibliographic record
Abstract
Evaluation of Protective Gloves and Working Techniques for Reducing Hand‐arm Vibration Exposure in the Workplace: Matija MILOSEVIC, et al . Electrical and Computer Engineering Department, Ryerson University, Canada— Objectives Operation of handheld power tools results in exposure to hand‐arm vibrations, which over time lead to numerous health complications. The objective of this study was to evaluate protective equipment and working techniques for the reduction of vibration exposure. Methods Vibration transmissions were recorded during different work techniques: with one‐ and two‐handed grip, while wearing protective gloves (standard, air and anti‐vibration gloves) and while holding a foam‐covered tool handle. The effect was examined by analyzing the reduction of transmitted vibrations at the wrist. The vibration transmission was recorded with a portable device using a triaxial accelerometer. Results The results suggest large and significant reductions of vibration with appropriate safety equipment. Reductions of 85.6% were achieved when anti‐vibration gloves were used. Conclusions Our results indicated that transmitted vibrations were affected by several factors and could be measured and significantly reduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".