PERHITUNGAN STATUS MUTU AIR BENDUNGANDI PROVINSI NUSA TENGGARA BARAT
Bibliographic record
Abstract
Penelitian ini menganalisis status mutu air Bendungan Batujai, Pandaduri, Pengga, Tibu Kuning, dan Telaga Lebur yang berlokasi di Provinsi Nusa Tenggara Barat (NTB). Kualitas air waduk dihitung dengan menggunakan Peraturan Pemerintah Lingkungan Hidup No 115 Tahun 2003 tentang Pedoman Penentuan Status Mutu Air. Sampling air dianalisis dari titik genangan dan sungai yang masuk ke bendungan tersebut. Hasil penelitian menyatakan bahwa kualitas air bendungan termasuk ke dalam kategori tercemar ringan sampai cemar berat dimana tingkat pencemaran terendah ada di Bendungan Telaga Lebur dan tertinggi di Bendungan Pengga. Pencemaran terjadi karena terdapat delapan parameter yang telah melebihi baku mutu yaitu TSS, DO, Ammonia, Nitrat, Nitrit, Besi, Mangan, dan Total Fosfat. Terdapat parameter yang memenuhi baku mutu karena ada aktivitas lain di sekitar bendungan yang dapat dilihat dari penggunaan lahan yang merupakan sumber pencemar. Dilihat dari setiap sumber pencemarnya, dapat disimpulkan bahwa jumlah TSS, BOD, COD, TN, dan TP di setiap bendungan paling besar bersumber dari permukiman, sehingga perlu adanya upaya pengendalian kualitas air berupa pengelolaan di sektor pertanian, persampahan, dan limbah domestik. Kata kunci: Bendungan, Nusa Tenggara Barat, Pencemaran, Status Mutu Air.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 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".