Water quality analysis and corrosion potential of the distribution network of Patna, Bihar, India
Bibliographic record
Abstract
Drinking water distribution system water quality can be affected by chemical and microbial processes. A change in the chemical concentration of water is a result of pipe scaling and corrosion. It causes insignificant deterioration of water quality. This study determined the corrosion and scaling potential of the drinking water in the distribution networks of the water supply in Patna City, Bihar, India. For the determination of the physico-chemical parameters, 92 water samples were collected from 46 points of the distribution network. Four parameters were analysed in situ – namely, temperature, electrical conductivity, pH and total dissolved solids – and the remaining parameters – alkalinity, total hardness, calcium, magnesium, chloride, sulfate, nitrate and iron – were measured in the laboratory. Various widely used indices – namely, Langelier saturation index (LSI), Ryznar stability index (RSI), Puckorius scaling index (PSI), Larson–Skold index (LS) and aggressive index (AI) – were used to calculate corrosion and scaling potential of water samples. A result of the LSI and RSI, show that 86.96% of water samples are corrosive and only 13.04% are scaling tendency. PSI shows 30.43% of water samples are corrosive. LS shows all water samples are mildly corrosive. AI shows 71.74% of the water samples are moderately corrosive and only 28.26% of the water samples are scaling tendency.
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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 | 0.000 |
| 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 | 0.000 |
| 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".