Water Quality Degradation in the Deli River Watershed, North Sumatra: Impacts of Land Use and Pollution Sources
Bibliographic record
Abstract
The Deli Watershed is crucial in Medan's hydrological cycle and the surrounding areas. It serves as a clean water source for Medan, but is also affected by urbanisation and industrial discharge. This study aims to assess water quality using pollution indices and spatial analysis across the Deli Watershed. Water samples were collected from the river in the watershed, North Sumatra Province, between June and July 2023. Observations were made at 46 spatial points through purposive sampling. These points represent the downstream (five points), middle (16 points), upstream (10 points), and tributaries (15 points). The pollution status of the basin was determined using the Pollution Index, the National Sanitation Foundation-Water Quality Index (NSF-WQI), the Canadian Council of Ministers of the Environment (CCME), and the SingScore Method. The spatial water pollution in the Deli Watershed varies from poor to good or excellent. The most severe river conditions are observed in the downstream parts of the Sei Sekambing and Deli sub-watersheds, characterised by moderately to poorly polluted water. Good or unpolluted river water quality was only found in 3 of 42 observation points, especially in the upper reaches of the Sembahe River and the Simai Mai River. Therefore, serious steps are needed from the government to restore and rehabilitate the Deli River Basin area, namely forest areas, plantations, and tourist areas, especially in the upper reaches of the Deli River Basin, namely in the Karo and Deli Serdang Regencies.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".