Chemical Analysis of Rock and Water from dug Wells in a Residential Area in Lagos State, Nigeria
Bibliographic record
Abstract
Samples of rock deposit dug out from wells at Alimosho local government in Lagos state, Nigeria were characterized for chemical composition and ultimate utilization. Fourier Transform Infrared Spectroscopy (FTIR), X-Ray diffraction (XRD) and Atomic Absorption Spectroscopy (AAS) were used to identify the rock as a mixed clay rock containing 35.04% Illite, 25.64% Illite plus Quartz, 22.20% Kaolinite and 17.09% Feldspar. This was corroborated by cation determination that showed the rock to be predominantly clayey rock. The mean concentrations of lead and copper in water of the wells in the rock location are 0.004mg/L and 0.05mg/L respectively while cadmium is less than detectable limit. The well water in the area studied is therefore not contaminated by these heavy metals. Heavy metals removal by the mixed clay from polluted underground water was studied using AAS. A general increase of sorption was observed with increase in the adsorbent dosage. 20g of the rock adsorbed a higher percentage of copper (80.23%) than lead and cadmium that were 70.25% and 65.83% respectively. The percentage of heavy metals adsorbed confirms the use of mixed clay minerals as a contaminants removal from the underground polluted water. The rock adsorbed copper readily than cadmium and lead. The maximum amount of lead removed from the contaminated underground water of the defunct battery site across the adsorbent dosage (5g, 10g, 15g and 20g) were 1.20mg/L, 3.19mg/L, 3.69mg/L and 4.09mg/L respectively indicating the effect of increase in adsorbent dosage in remediating the lead contaminated water. This data showed that the rock can be used to remove these heavy metals from contaminated water.
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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) | 0.001 | 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".