Characterisation of Local Borehole Water within the Platinum Belt inMokopane, Limpopo, Republic of South Africa
Bibliographic record
Abstract
South Africa is known to be a water scarce country, with an average rainfall of about 495 mm per annum.Around 40% of the world's population is affected by freshwater scarcity because of growing urbanization, industrialization, and excessive use of natural resources.With no adequate water infrastructures in place, rural areas in Mokopane Town rely on borehole water for everyday endeavours.The impact of Mining has subsequently led to the degradation of the water quality in the areas in which mines operate.Water pollution is one of the global concerns with the goal of improving water quality and reducing the negative effects on human and ecological health.The increase in mining activities over the Mogalakwena catchment possess a water quality risk due to mine water decant and fluorides caused by the geology of the area.By characterising the water samples in the area, the Physio-chemical analysis of the study showed most physical properties of the groundwater to be within the acceptable WHO standards.Chemical properties showed very high Mg values (As high as 139 mg/L) and Fluoride values as high as 2.05mg/L.Nitrate and Nitrite showed poor and unacceptable(>10mg/L) values on 69% of the water samples.This characterisation used techniques such as environmental assessment, Inductive coupled plasma optical emission spectrometry (ICP-OES), conductivity probe, water turbidity, total suspended solids method, ultraviolet visible spectroscopy, and pH.The results were analysed using GIS to map out the boreholes and their respective water results.It's recommended that Mogalakwena Municipality needs to monitor public boreholes in the area and upload results on ground water assessment for easier access on information, for the people to know the condition of the water they receive.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".