Assessing the Impact of an Operating Tailings Storage Facility on Catchment Surface and Groundwater Quality in the Ellembele District of the Western Region of Ghana
Bibliographic record
Abstract
<p>The study assessed the impact of an operating Tailings Storage Facility (TSF) of Adamus Resources Limited (Nzema Gold Mine) in the Ellembele District of the Western of Ghana on catchment surface and groundwater quality. Water samples were collected between June and December 2014 from seventeen (17) sampling sites including the TSF decant water (TSF-DW), three (3) streams, a water storage dam, a pond and eleven (11) groundwater monitoring boreholes within 500 m radius of the mine’s Tailings Storage Facility. Samples were analyzed for pH, true colour, electrical conductivity (EC), total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), dissolved metals (arsenic, cadmium, copper, mercury) and cyanide (weak acid dissociable cyanide (WAD), free cyanide and total cyanide) using standard procedures. The TSF-DW reported elevated arsenic, free cyanide and TSS concentrations above GHEPA guideline for effluent discharge. Elevated TSS and arsenic concentrations above GHEPA limits were reported in PWSD which is a pond uphill of the TSF and a receptor to effluents from illegal mining sites on the mine’s concession. All other parameters recorded in surface and groundwater bodies studied were within WHO guideline limit for potable water. Results of the study suggest that the quality of surface and groundwater around the TSF has not been adversely affected even though the TSF is contaminated. Study findings suggest that well-engineered tailings dam ofARLwith its effective liner and management systems may have provided a safe structure and prevented contamination of water resources within its catchment.</p>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".