Analysis of Irrigation Water Quality Indices for Chambal River at Kota, Rajasthan, India
Bibliographic record
Abstract
Surface water is an essential natural resource for the survival of biotic components on Earth and plays a vital role in every country's domestic, commercial, agricultural, and industrial sectors. River water is a primary source of surface water, and its quality is very important for crop production, maintenance of soil productivity, and protection of the environment; hence, the assessment of river water quality for irrigation is essential. The present study assessed the Chambal River water quality for irrigation purposes in upstream and downstream of Kota Dam, Rajasthan (India). Various irrigation water quality indices (IWQIs), such as SAR, KR, TH, Na%, PI, MH, RSC, and RSBC are evaluated to define overall irrigation water quality. Four sampling sites are selected to define IWQIs with the help of seven water quality parameters, namely, sodium, magnesium, potassium, calcium, carbonate, bicarbonate, and electrical conductivity. This study shows a significant variation in the values of different IWQIs for irrigation compared to 2019 in 2020. The values of IWQIs at Akelgarh indicate the water quality as "Good and Suitable" for irrigation purposes for both years. However, the scenario is different at Rangpur, SRRT, and Keshoraipatan sampling locations. The river water quality for irrigation is continuously deteriorating during the study period and presently has the status of "Moderately suitable" for irrigation. The research demonstrated the application of IWQI, which would be helpful to policymakers and stakeholders to provide large-scale management to control pollution in the Chambal River for economic and sustainable social development.
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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 | 0.001 | 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) | 0.017 | 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".