Status mutu air Kali Angke di Bogor, Tangerang, dan Jakarta
Bibliographic record
Abstract
Masuknya bahan-bahan pencemar ke dalam badan air sungai menyebabkan turunnya kualitas air sungai. Salah satu sungai yang diduga telah mengalami pencemaran adalah Kali Angke. Penelitian ini bertujuan untuk menentukan status mutu air dan tingkat pencemaran Kali Angke menggunakan metode Indeks Pencemaran (IP) dan Indeks Canadian Council of Minister of The Environment (CCME). Pengambilan data kualitas air dilakukan pada lima segmen sebanyak 21 titik pengambilan contoh pada tanggal 2–4 Oktober 2017. Data sekunder kualitas air berasal dari Dinas Lingkungan Hidup Kota Bogor, Kabupaten Bogor, Tangerang Selatan, Tangerang, dan Jakarta Barat. Parameter kualitas air meliputi parameter fisika (suhu, TSS, dan TDS), parameter kimia (DO, BOD, COD, NO2-N, NO3-N, pH, total fosfat, Zn, minyak lemak, Hg, dan Cu), dan parameter biologi (fecal coliform dan total coliform). Indeks kualitas air CCME lebih mewakili kondisi perairan daripada Indeks Pencemaran. Tingkat pencemaran semakin meningkat dari hulu ke hilir dan dari tahun 2014 sampai 2016, kemudian menurun pada tahun 2017. Status mutu Kali Angke tergolong cemar ringan menurut IP dan tergolong buruk menurut Indeks CCME.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".