A comparison between methods for assessment of whole-body vibration exposure: A case study in a limestone quarry
Bibliographic record
Abstract
The problem of the whole body vibration (WBV) exposure for operators in the workplace has been analyzed by many researchers that have studied for a long time the complex relationships between this phenomenon and human health risks.Previous studies have shown that drivers of different vehicles, in particular operators in mining activities, are afflicted by serious musculoskeletal injuries due to WBV exposure.Criteria in order to measure and assess equivalent daily exposure, associated with different operations of the vehicles, are referred to standards ISO2631-1, based on method of the frequencyweighted equivalent continuous root mean squared (r.m.s.) acceleration.Numerous studies point out the complexity to outline a clear relationship between WBV exposure and the human health disorders.An experimental campaign was realized in a limestone quarry near Rome and vibration measurements were recorded on four different mining vehicles (dumper, jeep, digger and puncher machine).The vibratory signal was taken at the seat operator/seat interface (X, Y and Z axes) in accordance with the ISO 2631-1 during daily quarry operations.The data obtained were elaborated and finally the results are presented in order to evidence the difference of WBV exposure between the specific operating tasks and some methods for the purpose of exposure dose reduction are suggested.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".