Risk assessment for loader- and dozer-related fatal incidents in U.S. mining
Bibliographic record
Abstract
The paper presents the results of research aimed at developing a risk assessment process that can be used to more thoroughly characterise risks associated with loader- and dozer-related fatal incidents in US mining. The assessment is based on historical data obtained from the US Mine Safety and Health Administration investigation reports, which includes 77 fatal incidents that occurred from 1995 to 2006. The Preliminary Hazard Assessment method is used in identifying and quantifying risks. Risk levels are then developed using a pre-established risk matrix that ranks them according to probability and severity. The resulting assigned risk value can then be used to prioritise risk control strategies. A total of 10 hazards were identified for loaders. The hazards 'failure to follow adequate maintenance procedure' and 'failure of mechanical/electrical/hydraulic components' were the most severe and frequent hazards and they fell into the category of 'high' risk. The same number of hazards was identified for dozers. The hazard 'failure to identify adverse site/geological conditions' was the most severe and frequent hazard and it fell into the category of 'high' risk.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".