Integration of OHS into Risk Management in an Open-Pit Mining Project in Quebec (Canada)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it