GEO-CHEMICAL CHARACTERIZATION OF HEAVY METALS IN GROUNDWATER AT OGALA COMMUNITY, ELEME LOCAL GOVERNMENT AREA OF RIVERS STATE, NIGERIA
Bibliographic record
Abstract
Groundwater samples were collected from nine boreholes in Ogale Community, Eleme Local Government Area of Rivers State.The aim is to characterize and evaluate heavy metal concentration in the area using Atomic Absorption Spectrometer, Colorimeter, and Turbidimeter.The result shows that Mg ranges from 2.6mg/l-3.40mg/l,Mn ranges from 0.01mg/l-0.038mg/l,Fe ranges from 0.32mg/l-2.3mg/l,Cd ranges from 0.001mg/l-0.003mg/l,Cr ranges from 0.001mg/l-0.007mg/l,Ni ranges from 0.012mg/l-0.02mg/l,Pb ranges from 0.001mg/l-0.011mg/l,Zn ranges from 0.022mg/l-0.024mg/lNa goes from 4.47mg/l-7.78mg/l,and Ca ranges from 0.003mg/l-0.014mg/l.Other parameters that were analyzed are pH, Salinity, Turbidity, Electrical Conductivity, Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Total Alkalinity (TA), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and Chemical Oxygen Demand (COD).When the results were compared to various Standards (WHO 2007, Canadian Standard, NAFDAC, SON, and BIS), it showed a high concentration of Cd and Fe.It is advised that regular checks of the boreholes should be carried out to ascertain water quality, quality pipes and faucets should be used to prevent contamination, and tools like lime softeners should be used for water remediation.
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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".