Climate change risks and vulnerabilities during mining exploration, operations, and reclamation: A regional approach for the mining sector in Québec, Canada
Bibliographic record
Abstract
Climate change (CC) has already and will continue to have a significant impact on the mining sector. A comprehensive and thorough analysis was performed to evaluate the risks and vulnerabilities resulting from CC during the exploration, operation, and reclamation phases of the mining life cycle. This analysis focused on six mining regions in Québec. Climate scenarios were produced for each region and, along with a literature review, used to assess the effects of CC on the mining sector. The results were presented to a panel of experts who highlighted the risks for each activity at each phase of the mining life cycle. A second group of experts evaluated the level of risk and the mining industry’s vulnerability for each risk identified by the first group. Six main risks due to CC were identified: activity schedule disruptions, loss or limitation of site accessibility, water management issues, instabilities and failures of storage facilities, operations infrastructure instabilities, and reduction of reclamation cover performance. The mining experts performed a Delphi-type survey for each mining activity to determine the risk level for each region, leading to the production of a risk matrix. Results indicate that CC is expected to particularly affect reclamation cover performance, and extreme climate events are projected to have significant impacts on operations. Vulnerability levels were assigned by a third group of experts based on the industry’s ability to adapt to these risks. Results showed a low vulnerability for exploration, low to high vulnerability for operations, and low to very high vulnerability for reclamation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".