UJI COBA PENGOLAHAN AIR WADUK MENJADI AIR MINUM DENGAN METODA KOAGULASI FILTRASI, DAN KLORINASI
Bibliographic record
Abstract
Human needs water to meet the main needs for drinking water. In some areas in Indonesia, especially Dusun Karangwungu, Gresik shortage of water still frequently happens. The absence of water treatment in the reservoir exists in the area prompted me to conduct a research on physical, chemical, and microbiology of the water to be processed as drinking water. This is a descriptive typed study and data were collected through the use of secondary data, laboratory examinations, and observations. After sampling, the sample was then given the treatment of coagulation, fitasion and chlorination. Laboratory results were then compared with Minister Regulation No. 492 of 2010. The objective of this study was to proceed the reservoir water into drinking water in accordance with the Minister of Health Regulation number 492 year 2010 on Drinking Water Quality. The results of the study showed reduction in 19 test parameters in accordance with Permenkes 492/2010. The results showed that the reservoir water can be used as raw material for drinking water by coagulation, filtration, and chlorination. To society is expected to use reservoir water into drinking water to meet the needs. Further research for additional parameters in accordance with the Health Minister Regulation 492/2010 and Breakpoint chlorination and Chlor absorbance Power in chlorination process can be carried out. Keywords : coagulation, filtration, chlorination of drinking water
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".