Influence of Mine Dewatering‐Effluent Cycling on Arsenic Loading in a Gold Mine Tailings Containment Area
Bibliographic record
Abstract
Abstract The Giant Mine (1948–1999) generated 16 Mt of Au‐bearing mill tailings (2800 mg kg −1 ) originating from a mixture of flotation tailings (84.8 wt%), calcine residues (14.4 wt.%), and arsenic trioxide roaster waste (0.8 wt.%). A water treatment system for high As mine dewatering effluent has operated since the end of mine operations, with the intermediate storage area being the Northwest Tailings Containment Area (NW‐TCA). The tailings porewater contains elevated concentrations of dissolved As, Sb, Zn, and other metals. A multi‐year water balance supported by an examination of unsaturated and saturated flow conditions and based on isotope and geochemical analysis was conducted to understand the NW‐TCA hydrological system. Hydraulic gradients indicate persistent downward flow in the south end of NW‐TCA. Water balance calculations indicate an average of 384,000 m 3 y −1 of water from the NW‐TCA entered the groundwater system during the study period (2017–2022). Stable water isotope and water chemistry measurements indicate porewater in areas of high‐water table is influenced by mine dewatering effluent. Isotope mass balance indicates 60% of the mine dewatering effluent, which contains high concentrations of As, is sourced from water cycled through the NW‐TCA. The impact of the mine dewatering effluent is minimal in areas with a deep vadose zone, containing lower concentrations of As. Hydrological simulations indicate groundwater flow rates into the deep groundwater flow system from the NW‐TCA would be modest in the absence of the mine dewater pond because the system is near net evaporative in the absence of mine dewatering effluent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".