Evaluation of Groundwater Suitability for Domestic and Irrigational Purposes: A Case Study from Mingoa River Basin, Yaounde, Cameroon
Bibliographic record
Abstract
A baseline study involving analyses of subsurface water samples from the Mingoa river basin (longitude: 11°30′E; latitude: 3°52′N) in migmatitic complex in Yaounde Cameroon (central Africa) was carried out to assess their suitability for drinking, domestic and agricultural purposes. Study results show that pH is ranged between 5.1≤ pH ≤ 5.8 and then, induces acidic waters. Groundwater samples are generally characterized by low conductivity values, of which 100% are within the range (55 ≤ EC ≤ 1500 μS/cm). The mean values of the major cations (Ca2+, Mg2+, Na+, K+) and anions (SO42– , Cl–, HCO3– ) are all within the World Health Organization (WHO) standards. Two of the springs sampled have nitrate (NO3– ) contamination. Even though contamination and acidic waters exist in some of the springs, the majority of the springs are excellent for agricultural and domestic purposes. Assessment of the groundwater for agricultural irrigation revealed two main categories. These are low salinity-low sodicity (C1-S1) and medium salinity-low sodicity (C2-S1), using the US Salinity Laboratory (USSL) classification scheme. As much as all of the samples plotted in the “excellent to good” and “good to permissible” categories on the Wilcox diagram. The groundwater in the study area may therefore be regarded as good for irrigation activities. The major identifiable geochemical processes responsible for the evolution of the various ions are mineral weathering, chemical reactions and anthropogenic activities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".