Suitability of water quality index methods for assessing groundwater quality in the Ganges River basin area
Bibliographic record
Abstract
Abstract This study considered three different water quality indices (WQIs) for groundwater data collected from the middle-west part of Bangladesh, and the quantitative results were justified with the globally accepted water quality guidelines. It compared the results between the Canadian WQI with the Weighted Average WQI, and the Canadian WQI and Mierels WQI for drinking and irrigation purposes, respectively. The results revealed that the Canadian method categorized water as ‘fair’ quality while the Weighted WQI model results showed ‘unsuitable’ for drinking usage. Besides, the Meireles method showed that the water quality is classified as ‘good’ to ‘excellent’, while the categorization of the groundwater using the Canadian method was ‘fair’ to ‘good’ for the suitability of irrigation. When comparing the results of the Canadian method with the Weighted Average technique for drinking, the latter one gave the abnormal results; and pair difference statistics showed the significant negative correlation (r=−0.91) between them. Similarly, the analysis of the two methods (Canadian and Mierels) for irrigation use exhibited that there was no statistical variance between the two techniques at a significant correlation matrix (r=+0.71). The study concluded that the Canadian WQI for drinking and Mierels WQI for irrigation would deliver better results.
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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 | 0.027 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".