Physicochemical Assessment of Rain Water of Karachi, Pakistan
Bibliographic record
Abstract
Local Precipitation (Rain) is a good source of surface water and could be a safe source of drinking water if it is free from contaminants. Many Asian countries do not have access to safe drinking water; therefore, they have no alternative but to use water from contaminated sources that poses a health hazard. In the present study, thirty three rain water samples were collected from Karachi, Pakistan during monsoon season of year 2007. The pH, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO) and hardness were immediately monitored after sample collections and possible sources of NH4, Na, K, Mg, Ca, F- , Cl- , NO3 - , CO3 -2 and SO4 -2 concentrations in rain water of Karachi city, Sindh, Pakistan were analyzed in order to distinguish safe source of drinking water. All samples were completely free from fluoride contamination while the concentration of chloride and sulfate was in range of 15.11-125 mg/l and 10.02- 72.02 mg/l indicate their presence from air pollution. Moreover, the study showed that the rain water can be harvested to extend potable and non-potable water supplies in this city.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".