Vibration exposure characteristics and health risk prevention strategies associated with vibration exposure during mining applications
Bibliographic record
Abstract
Occupational exposure to vibration can lead to health problems and is typically classified as whole-body vibration (WBV), hand-arm vibration (HAV), or foot-transmitted vibration (FTV). Vibration exposure characteristics are typically measured and compared to standards (ISO 2631-1; ISO 5349-1; EU Directive 2002/44/EC) in an effort to determine the probability of adverse health effects including: low-back pain, spinal degeneration, and gastrointestinal tract problems linked with exposure to WBV; decreased grip strength, tingling/numbness in the fingers/hands, and blanching of the fingers associated with exposure to HAV; and numbness/tingling in the feet/toes, and cold induced blanching of the toes stemming from exposure to FTV. The presentation will summarize vibration exposure data collected by the research team over a 10-year period which suggests operators of load-haul-dump vehicles, haulage trucks, and dozers are exposed to WBV above recommended guidelines, while operators of jack-legs are exposed to HAV above guidelines. Our data also suggest miners exposed to FTV associated with drilling off of raise platforms and operating bolters and jumbo drills are at risk of developing a vibration-induced injury. The presentation will also review the effectiveness of control strategies evaluated by the research team including seating, isolation platforms, “anti-vibration” drills, cab interventions, road maintenance, and operating speeds.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".