Occupational Hazards in Mineral Ore Crushing and Grinding: A Literature Review
Bibliographic record
Abstract
This article considers the problem of occupational health and safety in crushing and grinding mineral ore at the processing plants of mining enterprises.Occupational risks at workplaces are dust of complex composition (aerosol of fibrogenic action), industrial noise (80-105 dB) and vibration (up to 43-56% of working time), unfavorable microclimate.In the structure of occupational injuries, injuries of musculoskeletal system, concussions of the brain and contusions prevail.The aim of the study is to review the literature to identify occupational hazards of workers employed in ore crushing and grinding areas in order to compile a body of knowledge to investigate safety in the workplace.The study synthesizes theoretical statements and results of 38 scientific papers published between 2000 and 2023 in PubMed, Core Collection in Web of Science, ScienceDirect, and RINC databases to display the existing body of knowledge on occupational hazards in ore preparation for further processing in mining companies; PubMed database data were processed by VOS Viewer 1.16.18;descriptive analysis and textual narrative syntheses were compiled through a systematic literature review.Data on occupational risks of occupations such as plate feeder and conveyor (transporter, elevator) operators, crusher operators (jaw, cone, roller, hammer), screen and mill operators were obtained.The results of this study can provide useful information for making proposals for organizational and technical improvement to reduce occupational morbidity during ore preparation at concentrators and reduce the limitations of socio-economic consequences of occupational diseases and accidents.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".