Physico-Chemical Assessment of the Quality of Water of the Amansure River in the Western Region of Ghana
Bibliographic record
Abstract
The physico-chemical analysis of water samples from the Amansure River in the Western region of Ghana was investigated. The people living on the river have no access to treated water and depend solely on it for their daily activities. The river also receives untreated wastewater from nearby Industries, domestic and anthropogenic activities. The River was divided into section; lower, middle and upper section. Samples were taken from these sections and analyzed to determine the level of contaminations Complete water quality parameters considered in the investigation were pH, conductivity, Na + , K + , Ca 2+ , Mg 2+ , F - , NH 4 -N, Cl - , SO 42- , PO 4 -P, NO 2 -N, NO 3 -N, HCO 3- , total hardness as CaCO 3 , total alkalinity as CaCO 3 , Ca-hardness as CaCO 3 and Mg-hardness as CaCO 3. The result gave mean values of 5.6 mg/l, 0.14 mg/l, 8.137 mg/l, 0.304 mg/l, 3.893 mg/l, 1.568 mg/l, 0.001 mg/l, 0.005 mg/l, 10.660 mg/l, 6.399 mg/l, 0.077mg/l, 0.0668 mg/l, 0.112 mg/l, 0074 mg/l, 8.938 mg/l, 16.560 mg/l, 7.364 mg/l, 10.147 mg/l and 6.657 mg/l respectively. Similarly, soil analysis from the various sections gave concentrations of some heavy metals such as Zn, Pb, and As. Concentration of these heavy metals gave the results as 0.2159, 0.6136 and 0.0566 ppb for Zn, Pb and As respectively. The hydrochemistry of the Amansure River was also evaluated to access its suitability for agricultural purpose by plotting the major cations and anions such as Ca, Mg, Na, K, HCO 3 , SO 42- and Cl - in meq/l on the Piper diagram. It was realised the River is good for agricultural purposes. Key words: Amansure River, Contamination, Heavy Metals, Hydrochemistry, Sodium Adsorption Ratio
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".