Akumulasi Logam Berat Cd Pada Matriks Air, Sedimen, dan Ikan Nilem (Osteochilus hasselti) di Sungai Tajum Kabupaten Banyumas Jawa Tengah
Bibliographic record
Abstract
Kadmium (Cd) merupakan logam berat berbahaya yang dapat mengakibatkan terjadinya pencemaran di perairan. Penelitian ini bertujuan untuk mengetahui kandungan Cd pada matriks air, sedimen, dan ikan nilem (O. hasselti) di Sungai Tajum, Banyumas serta mengetahui tingkat pencemaran Cd berdasarkan Single Pollution Index (Pi), Contamination Factor (CF), Index of Geoaccumulation (Igeo), Bioaccumulation Factor (BAF), Estimated Daily Intake (EDI), dan Target Hazard Quotient (THQ). Metode penelitian yang digunakan adalah metode survei. Teknik pengambilan sampel menggunakan teknik purposive random sampling. Lokasi penelitian dibagi menjadi 5 stasiun dengan 3 kali ulangan pengambilan sampel. Hasil analisis data menunjukkan bahwa kandungan Cd pada air berkisar 0,009-0,030 mg/L, sedimen berkisar 0,320-0,533 mg/kg, dan ikan nilem berkisar 0,010-0,029 mg/kg. Kandungan Cd pada air telah melebihi Nilai Ambang Batas (NAB) menurut PP RI No. 22 Tahun 2021, kandungan Cd pada sedimen menurut pedoman Canadian Council of Ministers of the Environment (CCME) masih dalam NAB. Kandungan Cd pada ikan nilem tidak melebihi NAB yang ditentukan oleh Kepmen-KKP No. 37 Tahun 2019 dan BPOM No. 5 Tahun 2018. Tingkat pencemaran di Sungai Tajum menunjukkan kategori slight pollution – mild pollution berdasarkan Pi, tercemar sedang berdasarkan CF, berdasarkan Igeo tidak tercemar hingga sedang, berdasarkan BAF organisme memiliki kemampuan dan kurang mampu mengakumulasi Cd, berdasarkan EDI termasuk kategori tinggi, dan berdasarkan THQ terdapat resiko. Hal ini menunjukkan Cd akan semakin meningkat dan ikan hasil tangkapan pada Sungai Tajum apabila dikonsumsi secara terus menerus dalam jangka waktu yang panjang akan menyebabkan masalah kesehatan serius.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".