Forecasting and Sustainability of Raw Water Supply for Indonesia’s New Capital
Bibliographic record
Abstract
Environmental load issues in Jakarta, economic contribution to GDP, Java's dominance in land conversion, economic justice, and a water supply dilemma are all factors that contribute to Jakarta's excessive density.Therefore, the Indonesian government intends to relocate the Indonesia's capital to the Penajam Paser Utara district, in the East Kalimantan Province, in 2024.One of the challenges for the new capital, known as Indonesia's new capital (IKN), is securing a sustainable raw water supply for drinking.On the other hand, the potential of existing water resources is very limited in terms of resources and supporting infrastructure.This paper investigates the forecasting and sustainability of raw water supply for Indonesia's new capital.The hydrological feasibility method, which includes calculating the estimated supply and evaluating the sustainability of IKN raw water sources, was used in this study.The result showed the estimated raw water requirements for the IKN area during the period 2023-2073.The critical point of the resulting state is that the water resources of IKN by 2023 will be secure if the infrastructure plan is properly implemented.Meanwhile, IKN will be in deficit after 2045.Therefore, integrated water resource management is an important issue for the Indonesian government to minimize the limited raw water sources for IKN's sustainability.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".