DEVELOPMENT OF A VERSATILE WATER QUALITY INDEX FOR WATER SUPPLY APPLICATIONS
Bibliographic record
Abstract
Water quality index is an essential tool for water quality assessment.Considering the frequent use of automated water quality monitoring systems, their importance in generating data and information for the efficient management of water treatment plants, the design of such an index would allow the detection of point-pollution episodes, which otherwise would occur undetected and would have a significant impact on the water quality and its further treatment.In this study, a versatile weighted water quality index is presented.The proposed index is validated by using historical data recorded by the on-line monitoring system at the intake from Prut River during the period February -December 2012.The parameters selected for evaluation were: pH, temperature, turbidity, conductivity, dissolved oxygen, nitrates and total organic carbon.The weighted index is compared with the well-known Canadian water quality index.For the period under study, the scores for both indexes resulted in class IV of quality, corresponding to a medium water quality.The sensitivity analysis indicates a higher accuracy of the weighted index model as compared to the Canadian index model.The proposed index may be used as a communication tool for water quality towards the general public and various other water stakeholders, and as an operational control instrument for comparing water quality to diverse uses requirements (drinking water use in this study).
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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 | 0.000 | 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".