Identification and categorization of hazards in the mining industry: A systematic review of the literature
Bibliographic record
Abstract
Abstract Control of OHS risks in the mining industry has been attracting increasing attention in recent years. Because of their great diversity in a complex system, hazards can be difficult to identify and classify, especially when system components interact. Risk cannot be managed successfully without comprehensive investigation of all its aspects. A coherent and integrated classification for identifying and categorizing all hazards is currently lacking in mining. We propose an integrated system classification of OHS hazards in mining based on our review of 44 studies retrieved using PRISMA. Considering Canadian and international standards, regulations and conventions, new hazard categories are proposed and hazard prevention is discussed from 12 perspectives: physical, chemical, biological, ergonomic, accident and psychosocial risks, as well as policy, legislation, management, design, geography, and uncertainty, with reference to each of the four phases of a typical mine life cycle, the hazards were shown in a portrait. This paper provides suitable categories based on rational data for creating a portrait in order to OHS hazards prevention in life cycle activity in mine.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".