KANDUNGAN MERKURI DALAM BEBERAPA MEDIA SEKITAR PENAMBANGAN EMAS SKALA KECIL (PESK) DI KALIMANTAN TENGAH
Bibliographic record
Abstract
hasil pengukuran P3KLL pada tahun 1999-2008 dan hasil pengukuran terbaru yang dilakukan oleh Direktorat PB3-KLHK pada tahun 2018.Analisis Hg dilakukan menggunakan Mercury Analyzer Hg-5000 metode cold vapour, preparasi contoh uji sesuai dengan Standar Internasional Jepang (JIS) dan Standar Nasional Indonesia (SNI).Hasil penelitian menunjukkan bahwa pada sebagian besar lokasi sampling menunjukkan konsentrasi merkuri dalam air sungai masih berada di bawah standar Peraturan Pemerintah Nomor 82 Tahun 2001 untuk air kelas I (0,001 mg/L), namun di 2 lokasi lebih tinggi daripada standar tersebut.Merkuri yang terkandung dalam sampel ikan air tawar dari sungai di sekitar PESK, ditemukan pada kisaran 0,08 -0,224 mg/kg.Nilai ini masih berada di bawah nilai persyaratan kontaminasi logam pada ikan sesuai SNI:7387 2009 (0,5 mg/kg).Kandungan merkuri dalam kisaran sedimen antara 0,0291 -0,45 mg/kg, di mana beberapa lokasi sudah berada di atas nilai baku yang di atur dalam Quality Guidelines for Freshwater of Canadian Environmental Quality (CEQ), yaitu 0,17 mg/kg.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".