PENERAPAN METODE CCME-WQI UNTUK MENGANALISIS KUALITAS AIR DANAU DI PESISIR KAMPUNG YOBEH DISTRIK SENTANI KABUPATEN JAYAPURA
Bibliographic record
Abstract
This research aims to analyze the water quality of lakes on the coast of Kampung Yobeh, Sentani District, Jayapura Regency using the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) method. The analysis was carried out using eight water quality parameters which were compared with clean water quality standards in accordance with Government Regulation Number 22 of 2021 concerning the Implementation of Environmental Protection and Management (Appendix VI, Class 1). Sampling was carried out temporally in January, May and September 2024 to understand variations in water quality over different time periods. The research results show that the CCME-WQI value obtained is 24.177, which is included in the "Poor" category. Of the eight parameters analyzed, five of them did not meet clean water quality standards, so the lake water quality was not suitable for use as a raw water source for drinking water. These findings indicate the potential for pollution which could have a negative impact on aquatic ecosystems and the health of surrounding communities. Therefore, efforts to manage and mitigate pollution are needed to improve the quality of lake water in the region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".