KOMPARASI FUNGSI DEKONTAMINASI PERSONEL PELETON NUBIKA INDONESIA DAN AMERIKA
Bibliographic record
Abstract
Era perang modern akhir-akhir ini dengan perkembangan Iptek dibidang kimia, biologi, radiologi, nuklir, dan bahan peledak, berimplikasi terhadap meningkatnya ancaman senjata CBRNE. Peleton Nubika sebagai ujung tombak di lapangan memegang peranan penting dalam menentukan keberhasilan tugas. Penelitian kualitatif menggunakan teknik studi literatur dan teknik komparatif untuk mendeskripsikan perbandingan dari Peleton Nubika Denzi Nubika Pusziad dengan Decon PLT (Heavy) 20th CBRNE Command. Peleton Nubika dibawah Komando Denzi Nubika dengan satuan pusat nubika Pusziad disiapkan untuk OMP dan OMSP serta bekerjasama dengan instansi nubika terkait. Decon PLT (Heavy) dibawah Komando Area Suport Company dengan satuan pusat Nubika-Jihandak 20th CBRNE Command disiapkan untuk OMP, OMSP dan perbantuan sipil serta memiliki satuan HQ dibidang nubika. Kapabilitas Peleton Nubika lebih bersifat umum dengan salah satu fungsi dekontaminasi didalamnya, dibandingkan Decon PLT (Heavy) dengan tugas khusus dekontaminasi. Operasional Dekontaminasi Personel Peleton Nubika sebanyak 11 stasiun, sedangkan Decon PLT (Heavy) sebanyak 8 stasiun. Optimisme Pemerintah terhadap ancaman senjata nubika berupa peningkatan satus satuan nubika, strategi kekuatan dan kemampuan pertahanan militer dan nirmiliter, serta konsep pembentukan satuan pelaksana nubika tiap Kodam hingga pusat logistik nubika.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".