Determination of Ground Water Quality for Agriculture and Drinking Purpose in Sindh, Pakistan
Bibliographic record
Abstract
The study was conducted to assess the quality of ground water from different Talukas of district Tando Muhammad Khan for drinking and agriculture purpose. Water samples for determining the water quality were collected in one liter polyethylene bags by observing standard sample collection method. It was ensured that sample collection sites must be at least 500 feet away from each other.Physical and chemical parameters of ground and surface water samples such as pH, Electrical Conductivity (EC), Turbidity, Colour, Taste, Odour, Alkalinity as CaCO3, Bicarbonate (HCO3), Carbonate (CO3), Calcium (Ca), Magnesium (Mg), Hardness, Sodium (Na), Potassium (K), Chloride (Cl), Phosphate (PO4), Total Dissolved Solids (TDS) and Arsenic (As) were determined.The study clarified that pH and odour was within the permissible limits in majority of samples whereas, Arsenic (As),Hardness, Sodium (Na),Total Dissolved Solids (TDS), Taste, Chloride (Cl) and turbidity were beyond the permissible limits set by WHO.The groundwater status in Tando Muhammad Khan district, TDS in 50% samples, Chloride in 54.16% samples, Sulphate in 44.8% samples, Calcium in 38.5% samples, Sodium in 54.16% samples, hardness in 21.88% samples were beyond the WHO’s permissible limits for human consumption.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".