Model of Sustainable Drinking Water Governance at Tirta Kualo Regional Drinking Water Corporate in Tanjungbalai City, Indonesia
Bibliographic record
Abstract
Tirta Kualo is the only regional drinking water corporate (herein after PDAM Tirta Kualo) that provides drinking water to the people of Tanjungbalai city, which has yet to be able to serve the entire community. The purpose of this study was to determine the potential availability of the Silau river, to meet the water needs of the people, to know the condition of drinking water supply governance of PDAM Tirta Kualo, and to design a model of sustainable drinking water governance at PDAM Tirta Kualo, Tanjungbalai city. A mixed method approach was used in this study. The population was customers of PDAM Tirta Kualo who have been domiciled in Tanjungbalai city for more than 10 years. The analysis shows that based on the area of Tanjungbalai city around 60.62 km2 with population 169.367 inhabitants and coverage services of PDAM Tirta Kualo in 2019 was only 68.86%. The projection of drinking water supply demand for each house connection in the next 5 years, assumed to be 2% increase, is an average of 14.79 liters/second. The condition of drinking water supply governance in PDAM Tirta Kualo is still low, and it is proven that there are still many issues in all working units. The design of a sustainable water supply governance model in PDAM Tirta Kualo refers to the regional spatial plan of Tanjungbalai city year 2013 until 2033.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".