POTENSI DUAL-USE BUILDING DALAM MENDUKUNG DARURAT BENCANA
Bibliographic record
Abstract
Saat ini, beberapa bencana akibat gempa bumi, banjir, terorisme hingga peperangan, silih berganti terjadi di beberapa negara, menimbulkan dampak negatif yang cukup signifikan yang mengancam keselamatan makhluk hidup hingga infrastruktur penting negara. Penerapan bangunan dual-use dapat menjadi solusi ketahanan dan titik lemah kerawanan dalam penanganan kebencanaan. Penelitian secara literasi ini bertujuan untuk menampilkan implikasi potensi bangunan dual-use pada aktivitas darurat akibat bencana alam dan bencana sosial meliputi integrasi fungsional dan teknologi, keamanan dan ketahanan, kemudahan pemeliharaan dan kerawanan. Integrasi fungsional dual-use (tujuan sipil dan tujuan militer) dan teknologi pintar IoT berdampak signifikan dalam efektifitas penanganan bencana. Listrik dan internet sebagai sumber daya utama, rentan cyber criminal dan perlu ketangguhan keamanan siber secara berkelanjutan. Inovasi teknologi konstruksi meningkatkan karakteristik struktural dan nonstruktural dual-use building untuk tujuan mitigasi maupun saat bencana. Teknologi IoT dan teknologi konstruksi berdampak penghematan jangka panjang bangunan serta peningkatan fungsional yang keberlanjutan. Kerawanan bangunan dual-use dapat dianggap sebagai sasaran militer secara keseluruhan atau entitas yang terpisah dan bahkan berbeda. Penggunaan dual-use pada bangunan yang dilindungi sebagai objek sipil berakibat kehilangan fungsi perlindungannya.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.015 |
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".