Mathematical Model to Predict Conductive Properties of Contaminated Riverbed Sand in Ado-Odo Ota Local Government Area of Ogun State, Nigeria
Bibliographic record
Abstract
The possibility of contamination is especially rising due to the increase in the number of industries in the Local Government Area. In this study, riverbed sands were collected from five major rivers in Ado-Odo Ota Local Government Area, and conductivity properties were determined after the samples have been treated with varying concentration of petrol, engine oil, diesel, caustic soda and H2SO4. HANNAN Electrical Conductivity Meter, KD2 Thermal Conductivity Meter and Constant Head Method were used to determine the electrical, thermal and hydraulic conductivities respectively. A mathematical model was developed that describes the effect of contaminants on the electrical (a), thermal (r) and hydraulic (k) conductivities of riverbed sand from the major rivers in Ado-odo Ota Local Government Area. The model equation incorporates the bulk density of the riverbed sand samples, as well as the concentration and conductivity of the contaminants as follows:r = 0.107x1+ 0.10x2 – 0.017x3 + 1.673, s = 1.911x1 + 18.229x2 – 0.015x3 + 47.173 and k = 0.056x1 + 0.381x2 – 0.031x3 + 0.162, where x1, x2 and x3 are bulk density of samples, conductivity and concentration of contaminants respectively. From interpolation analysis, sample from Ilogbo river contained about 30 ml/kg of engine oil, Mosafejo river contained about 10 ml/kg of caustic soda, Ijako river contained about 20 ml/kg of caustic soda, Iju river contained about 10 ml/kg of diesel and Igbogbo river contained 10 ml/kg of H2SO4, thus showing clearly how waste products from industries end up as contaminants in nearby rivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 teacher head, 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".