DINAMIKA TEMPORAL KANDUNGAN MERKURI TERLARUT, TERENDAPKAN DAN TERSUSPENSI DI PERAIRAN ESTUARI KAPUAS KECIL, KALIMANTAN BARAT
Bibliographic record
Abstract
Secara fisik, logam berat merkuri dalam perairan terdapat dalam fase terlarut, tersuspensi dan terendapkan. Konsentrasi merkuri dalam tiga fase tersebut sangat dinamis dan sangat menentukan konsentrasi logam dalam biota. Penelitian ini telah dilakukan pada April-Mei 2011 (musim peralihan I) dan September-Oktober 2011 (musim peralihan II) di Perairan Estuari Kapuas Kecil. Pengambilan sampel air dan sedimen dilakukan sebanyak 26 stasiun, 3 stasiun di sungai dan 23 stasiun di estuari. Analisis merkuri terlarut, terendapkan dan tersuspensi menggunakan CVAAS (Cold Vapor Atomic Absorption Spectrophotometer). Hasil penelitian menunjukkan bahwa kandungan merkuri terlarut, terendapkan dan tersuspensi dipengaruhi oleh musim. Kandungan merkuri terlarut dan terendapkan di perairan Estuari Kapuas Kecil pada musim peralihan I lebih rendah dibandingkan musim peralihan II sedangkan konsentrasi merkuri tersuspensi lebih besar pada musim peralihan I dibandingkan musim peralihan II. Berdasarkan pada kriteria baku mutu laut nasional menurut Kemen LH No. 51 Tahun 2004 dan CCME (Canadian Council of Ministers of The Environment), kandungan logam merkuri terlarut dan terendapkan di Perairan Estuari Kapuas Kecil umumnya relatif rendah dan masih dibawah ambang batas aman bagi kehidupan biota
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".