The Mitigation of Acid Rock Drainage: Four Case Studies from British Columbia
Bibliographic record
Abstract
A large number of mines throughout the world are faced with the high cost and technical challenges associated with ARD mitigation. The objective of this paper is to use four different British Columbia mines to describe the challenges mines face, generic components of cost effective mitigation, and site-specific approaches to reduce environmental risk and the post-mining liability. Potential mitigation strategies for individual mine components should be evaluated in terms of how well they must perform, how long they must last, what they will cost, compatibility with site conditions and their contribution to the cumulative risk and liability of the site. A key part of cost effective mitigation is gaining the necessary understanding of both the site and mitigation measures. A mine can dramatically reduce mitigation costs by using supportive site attributes and by making mitigation an integral part of the mine plan. Recognition of closure issues early in the mine life enables a mine to use operating facilities and personnel to address closure issues and run long-term, large scale tests under actual field conditions.
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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 | 0.000 | 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 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".