Analyses of sustainable indicators of water resources for redesigning the health promoting water delivery networks: A case study in Sahneh, Iran
Bibliographic record
Abstract
Healthy water is our prime demand however population explosion and industrialization have threatened the quality of water. Consequently, about a billion people in developing countries including Iran are struggling for a safe and sustainable water supply. Timely water sampling and analyses are critical to access and maintain healthy status. The current study investigates the state of water supply in 29 villages of Sahneh town and provides recommendations for maintaining good health. Water samples were extensively analyzed for the physical and chemical indexes using the EPA standards and the Iran national water standards (Table S1). The mean of pH, total dissolve solid, electrical conductivity, chloride concentration, sulfate, temperature, bicarbonate, total alkalinity, calcium hardness was 8.2, 326.5 mg/L, 422.4 mS/cm, 203 mg/L, 6.4 mg/L, 24.7 °C, 257.2 mg/L, 210.9 mg/L as CaCO 3 , 233.8 mg/L CaCO 3 , respectively that are within the permitted limit. Interactions between these factors were statistically analyzed to characterize the water samples. All sampled waters were probable to sediment according to the Langelier index (0.67 ± 0.20), corrosive according to aggressiveness (10.74 ± 0.40) and Puckhorius indexes (6.96 ± 0.63). Water samples also exhibited scaling therefore it is recommended to use cemented pipes for dispensing networks. Moreover, balancing pH, alkalinity, calcium levels and annual testing by the government should be considered to promote good health.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".