Clean water by design—The impact of landform design on long-term water stewardship
Bibliographic record
Abstract
Mine Rock Stockpiles (MRSs) typically represent the majority of acidity generation potential at a mine site, a multigenerational water quality risk in respect of metal leaching and acid rock drainage (ML-ARD).Too frequently the extent of this risk is unrecognised and underfunded, resulting in a need for increased effluent collection and water treatment capacity after closure, and frequently, in perpetuity.Conventional water treatment options like lime treatment, result in a precipitated waste sludge, which then also requires a longterm disposal and remediation plan.Mine closure practitioners, when taking a long-term, full-lifecycle approach to design, appreciate that source term control is the highest level of hierarchical risk control in respect of source-pathway-receptor risk management.Applying source term control philosophy to MRS landform design presents an opportunity to reduce ML-ARD risk and the associated water treatment costs.Integration of passive, or semi-passive water treatment solutions into the mine affected landscape can further increase the value of this approach.This paper compares capital and operating costs using conventional mine rock placement methodologies paired with conventional effluent collection and treatment against an integrated mine closure landform and passive water treatment design.The value of the proposed integrated approach will be demonstrated not just through discussion of costs, but also the value of honouring and respecting water's sacred place in Indigenous knowledge systems.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".