Assessing Sensitivity of Subsurface Mine-Dewatering Activities to Climate Change
Bibliographic record
Abstract
Underground mining activities require constant removal of groundwater from their void \nspaces to maintain dry and safely accessible excavations for ore extraction in a process known \nas dewatering. The nature and degree of dewatering activities is heavily dependent on the \namount of groundwater that can reach the mined areas and, conversely, dewatering of mines \nhas a significant effect on the regional groundwater flow in their vicinity. Furthermore, \ngroundwater supply to mining areas may be linked to climatic conditions, connected surface \nwater bodies and the geologic structures that link the surface and subsurface. Typically, fully \nsaturated groundwater models are used in industry to simulate site conditions and to plan mine \ndewatering infrastructure. These models often take historical climate averages into account \nwhen determining recharge to groundwater from the surface, and they do not typically account \nfor feedback between groundwater and surface water systems. While this has been sufficiently \naccurate in the past, it is expected that the progression of climate change will yield future \nestimates of groundwater recharge that differ significantly from historical averages. These \nchanges may be captured more accurately with fully coupled methods of simulating \ngroundwater and surface water simultaneously in areas with large surface water bodies \noverlying aquifers, or in locations where geologic structures provide significant preferential \npathways between the surface and subsurface. \nHere, we explored the changes in predicted dewatering rates for a real mining property \nin Central Quebec when considering a typical, industry standard fully saturated groundwater \nmodel with historically averaged recharge and a fully integrated groundwater/surface water \niii \nmodel that incorporates results from future climate scenarios. The two models were \nconstructed, parameterized, and calibrated for a site in Central Quebec. Simulations of \nunderground mine dewatering were run with both models, and the predicted dewatering rates \nfrom each model were compared. Simulation results demonstrated that the fully integrated \ngroundwater/surface water model with future climate yielded an estimated rate of dewatering \nthat was approximately 2% higher than that predicted by the fully saturated groundwater \nmodel with historical climate averages.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".