Geochemical Analysis of Domestic Groundwater Sources in a Suburb of Ota, Southwestern Nigeria
Bibliographic record
Abstract
Groundwater as one of the most valuable natural resources supports human health, economic development and ecological diversity. The activities of many constituents related to groundwater such as micro-organisms, gases, inorganic and organic materials can lead to contamination of well water, municipal drinking water sources and the environment. Therefore, geochemical analyses of domestic groundwater sources within Iju, Southwestern Nigeria have been conducted to determine the groundwater properties of water samples from available hand-dug wells and boreholes within the area. Fifteen (15) water samples sourced from wells and boreholes within the study area were analyzed for their major trace elements using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) in the laboratory and physiochemical characteristics such as pH, total dissolved solids and conductivity were calculated to determine the suitability of such water for agricultural and domestic consumption. The bulk analysis on the water samples revealed that majority of the trace elements when compared with the World Health Organization (WHO) drinking water standards and Acme Laboratory Canada Method Detection Limits (MDL) have values that agree with the recommended limit. These findings show that the present status of groundwater in Iju is good for domestic and agricultural purposes. However, the presence of trace elements such as Lead, Bromide, Copper, Potassium, Manganese, Rubidium and Silicon in fairly high quantities in the groundwater samples which may be as a result of treatment in the water or dissolved salts in the groundwater may constitute major health hazards if not quickly checked. In spite of groundwater studies done in Ota, important information on groundwater resources in Iju is still largely unavailable. Thus, groundwater management can be effectively planned based on these results for the study area.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".