Analysis of Water Quality and Pollution Level in Air Hitam River Pekanbaru City Using CCME Index
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menganalisis kualitas air dan tingkat pencemaran di Sungai Air Hitam, Kota Pekanbaru, Provinsi Riau menggunakan indeks Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI). Penelitian dilakukan pada bulan September 2024 dengan metode survei dan pengambilan sampel dilakukan secara purposive sampling di tiga stasiun yang merepresentasikan aktivitas manusia berbeda di sepanjang aliran sungai. Parameter yang diamati meliputi fisika (suhu, TSS, kecerahan, kedalaman, kecepatan arus), kimia (pH, DO, BOD, COD, nitrat, fosfat), dan biologi (total coliform). Pengukuran dilakukan di lapangan dan analisis laboratorium mengacu pada standar SNI dan PP No. 22 Tahun 2021. Hasil menunjukkan bahwa parameter BOD dan total coliform secara konsisten melebihi ambang batas. Skor indeks CCME-WQI masing-masing adalah 44,35 (Stasiun 1), 26,02 (Stasiun 2), dan 44,18 (Stasiun 3), yang menempatkan status mutu air pada kategori “buruk.” Penurunan kualitas air dipengaruhi oleh aktivitas domestik, industri, dan pertanian di sekitar sungai. Diperlukan upaya pengelolaan yang lebih lanjut untuk perbaikan kualitas air Sungai Air Hitam.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".