CCME-WQI and TM-WQI based Assessment for Groundwater Quality in Garividi region of Vizianagaram District, Andhra Pradesh, India
Bibliographic record
Abstract
The Canadian Council of Ministers of the Environment method (CCME) and the Logarithmic Aggregation method proposed by Tiwari and Mishra (TM) were used to assess the groundwater quality for drinking in Garividi region (Gurla, Gajapatinagaram, Dattirajeru, Merakamuidam, Cheepurupalli and Garividi mandals) which occupy some central part of Vizianagaram district of Andhra Pradesh, India. The groundwater samples were collected during every month from November 2018 to October 2019 from 38 bore wells of selected sampling locations in the study area. Fourteen parameters such as pH, EC, TDS, TH, TA, Ca2+, Mg2+, Na+, CO32-, HCO3-, Cl-, SO42-, NO3-, F- of samples were analyzed using standard laboratory procedures. This paper describes the assessment of ground water quality for drinking purposes using CCME and TM water quality index methods and water quality parameters variation with regression correlation analysis during Post and Pre monsoons and monsoon. From CCME-WQI analysis, it is observed that quality of about 2.63% of the water samples is Excellent, about 28.95% of the water samples is Good, about 52.63% of the water samples is Fair, about 5.26% is Marginal and remaining 10.53% is Poor and from TM-WQI analysis, it is observed that quality of about 2.63% of the water samples is Excellent, quality of about 42.1% of the water samples is good, about 39.5% is Medium, about 13.2% is poor and remaining 2.63% is unsuitable for drinking purpose in the study area. From the correlation analysis, it is observed that EC and TDS are the important parameters, as they are significantly correlated with other parameters.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".