Analyses of sustainable indicators of water resources for redesigning the health promoting water delivery networks: A case study in Sahneh, Iran
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it