PENENTUAN KADMIUM DALAM PRODUK PERIKANAN DENGAN GRAPHITE FURNACE ATOMIC ABSORPTION SPECTROMETRY
Bibliographic record
Abstract
Penentuan kadmium dalam produk perikanan telah dilakukan menggunakan Graphite Furnace AtomicAbsorption Spectrometry (GF-AAS) dengan magnesium nitrat sebagai matrix modifier, platform atomizationtype A dengan sensitifitas yang tinggi untuk pengukuran kadmium, dan koreksi latar belakang Zeeman. Metodeanalisis telah divalidasi berdasarkan parameter-parameter kimia analitik. Sebanyak 0,5 g sampel produkperikanan didestruksi menggunakan microwave digestion systems dengan menambahkan 5 mL asam nitrat pekatdan 2 mL hidrogen peroksida 30%, kemudian larutan hasil destruksi diencerkan hingga 25 g. Dari larutan inidibuat sederet larutan untuk pengukuran secara adisi standar, dan diukur dengan GF-AAS. Akurasi metodedilakukan dengan menganalisis bahan acuan bersertifikat DORM 3 Fish Protein Certified Reference Materialfor Trace Metals dari National Research Council Canada dengan nilai recovery sebesar 99,9 ± 0,8%. Dari hasilpenelitian ini diperoleh kadar kadmium dan ketidakpastiannya sebesar 0,273 ± 0,025 mg kg-1berdasarkan beratkering.Kata kunci: GF-AAS, Microwave digestion systems, kadmium, validasi metoda
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".