IDENTIFIKASI RADIONUKLIDA PEMANCAR GAMMA DI DAERAH PANTAI LEMAHABANG MURIA DENGAN SPEKTROMETRI GAMMA
Bibliographic record
Abstract
lDENTIFIKASl RADIONUKLlDA PEMANCAR GAMMA DI DAERAH PANTAI LEMAHABANG MURIA DENGAN SPEKTROMETRI GAMMA. Rencana lokasi terpilih Pusat Listrik Tenaga Nuklir (PLTN) yang akan dibangun terletak di sekitar pantai Lemahabang semenanjung Muria Jawa Tengah. Dalam rangka antisipasi kemungkinan terjadi perubahan radioaktivitas karena pembangunan PLTN, perlu dipersiapkan data awal konsentrasi radioaktiviras alami di daerah tersebut. Tujuan utama identifikasi ini dilakukan untuk mengetahui radioaktivitas gamma serta radionuklida lingkungan yang terdapat dalam cuplikan algae, ikan kerapu, sedimen dan air laut. Pengambilan cuplikan, preparasi maupun analisisnya mengacu pada prosedur analisis cuplikan radioaktivitas lingkungan. Instrumen yang digunakan adalah Maestro II EG&G spektrometer γ Ortec dengan detektor Ge(Li). Hasil identifikasi radionuklida alam pemancar γ dengan teknik spektrometri γ menunjukkan ada 6 jenis radionuklida alam yang teridentifikasi yaitu Ra-226 (186,52 keV), Pb-212 (238,75 keV), Pb-214 (395.94 keV), Tl-208 (583,19 keV), Ac-288 (911,07 keV) dan K-40 (1460,7 keV). Radioaktivitas γ untuk semua radionuklida dalam air laut masih jauh di bawah nilai batas radioaktif lingkungan menurut SK DIRJEN BATAN No 294/DJ/1992. Radioaktifitas pemancar γ tertinggi dalam air laut adalah K-40 terukur dengan konsentrasi 5,798 ± 0,537 Bq/L, sedangkan konsentrasi tertinggi yang diijinkan 104 Bq/L.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".