A novel approach for modelling water quality at mine closure
Bibliographic record
Abstract
Diavik Diamond Mines Inc (DDMI) is located in the diamond-rich Lac de Gras region in the Northwest Territories, Canada. DDMI has mined diamonds from three relatively narrow open pits with connecting underground mine tunnels that extend several hundred metres below Lac de Gras. During mine closure, the open pits will be refilled with Lac de Gras water and reconnected to the lake by breaching the dykes around the pit lakes to restore fish habitat. As well as accommodating ongoing mine-affected site runoff, groundwater and pit wall leachate, one of these pits will be used to dispose of process kimberlite (PK) which will release porewater for several hundred years after mine closure. Given the complexity of these subsurface pit lakes, interconnected mine tunnels, consolidating PK and lake hydrodynamics, demonstrating the suitability of water quality and the stability of chemoclines in pit lakes is required to meet closure criteria and obtain regulatory approvals. Regulatory requirements include demonstrating that the proposed closure plan would meet the following objectives: Water quality in the pit lakes and receiving environment allows for current and future water uses. Waste is prevented and/or minimised. The amount of waste to be deposited to the receiving environment is minimised (i.e. there is longterm chemocline/thermocline stability within the pit lakes to demonstrate long-term stratification) Lake water volumes used to fill the pit lakes do not adversely affect flow in the downstream environment. While quantifying the potential effects of closure under varying conditions is critical to obtaining regulatory approvals, sufficiently sophisticated modelling platforms to simulate the hydrodynamic, thermodynamic and water quality effects of closure conditions in such expansive and complex morphological systems within a reasonable time frame are not available. A lack of suitable modelling approaches was demonstrated through extensive testing of various modelling platforms. To balance the regulatory expectations for robust demonstration of proposed closure solutions, a new modelling approach was required. To be able to accurately estimate water quality, a comprehensive 3D hydrodynamic model was developed and linked to 1D and 2D models to capture the hydrodynamic processes required to predict the fate of water quality parameters in the pit lakes and Lac de Gras. Harnessing the strength of individual modelling platforms was the only approach to defensibly address regulatory concerns as well as meet set time frames. As an integrated platform, the model incorporated the proposed water management plan during operations and closure phases, the design and location of breaches connecting the pit lakes with Lac de Gras, water quality in pit lakes and water quality predictions for mine water discharges. This study presents the approach used to overcome modelling challenges due to this unique environment and describes methods used to integrate platforms to address regulatory requirements in a timely manner.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".