Reporting water quality of sustainable traditional ponds using water quality index in Nagaur district of western Rajasthan, India
Bibliographic record
Abstract
The present study investigates ancient water harvesting techniques of western Rajasthan and reports potable water quality by using the Water Quality Index (WQI). These small rain-fed ponds are still a significant water resource for the village community and quench the thirst for 6 to 10 months. Therefore, it is necessary to evaluate the water quality and its suitability for drinking, development of forest and irrigation purpose. There were four sampling sites that were analyzed seasonally for physico-chemical parameters including, pH, TDS, Fluoride, Nitrate, Chloride, Total Alkalinity, Total Hardness, Calcium and Magnesium over a period of one year from July, 2020 to June, 2021 in Nagaur district, Rajasthan. In the results, WQI indicated very poor and unsuitable drinking water quality in all seasons in the first sampling site located in the city due to poor management. Whereas, all the village sampling sites had excellent water quality index in terms of drinking in all seasons excluding one sampling site, which was showing poor water quality in summer. Calculations for WQI show that fluoride is the most influencing parameter in the study. The findings significantly enhance the understanding of the importance of these small water ponds and provide a base for making sustainable water strategies in present study areas.
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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 | 0.001 |
| 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 | 0.001 |
| 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".