Integration of OHS into Risk Management in an Open-Pit Mining Project in Quebec (Canada)
Bibliographic record
Abstract
Despite undeniable progress, the mining industry remains the scene of serious accidents revealing disregard for occupational health and safety (OHS) and leaving open the debate regarding the safety of its employees. The San José mine last collapse near Copiapó, Chile on 5 August 2010 and the 69-day rescue operation that followed in order to save 33 miners trapped underground show the serious consequences of neglecting worker health and safety. The aim of this study was to validate a new approach to integrating OHS into risk management in the context of a new open-pit mining project in Quebec, based on analysis of incident and accident reports, semi-structured interviews, questionnaires and collaborative field observations. We propose a new concept, called hazard concentration, based on the number of hazards and their influence. This concept represents the weighted fraction of each category of hazards related to an undesirable event. The weight of each category of hazards is calculated by AHP, a multicriteria method. The proposed approach included the creation of an OHS database for facilitating expert risk management. Reinforcing effects between hazard categories were identified and all potential risks were prioritized. The results provided the company with a rational basis for choosing a suitable accident prevention strategy for its operational activities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".