Introduction of Innovative Equipment in Mining: Impact on Occupational Health and Safety
Bibliographic record
Abstract
Occupational health and safety in mining has clearly improved in developed countries over the past twenty years, but accidents and illness still occur with unacceptable frequency. The arrival of new mining equipment, bigger, more powerful and complex and requiring a higher skill level appears also to increase certain specific risks of accident and work-related illness. The objective of this paper is to examine the impact of new equipment on occupational health and safety in underground mining. The injury rate associated with eight equipment introduction projects was examined. The results show clearly that the introduction of new equipment with technological innovations does not automatically reduce the injury rate. The new equipment may even generate a higher injury rate than the equipment it replaced. Ergonomic deficiencies were noted in some of the new equipment. We suggest that future research focus on identifying the mechanisms and conditions that determine injury rate following the acquisition of innovative as means of improving occupational health and safety in mining. Successful implementation of new mining equipment appears to depend on the specific conditions of use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".