Bibliographic record
Abstract
과학기술의 발달은 문명의 편리성을 동반하였으나 더불어 원자력 및 가스 등의 폭발, 유출사고 발생 그리고 세계각지에서 화생방 테러 발생 가능성을 가지고 있어 총괄적 대응노력이 국가적으로 시급한 과제로 대응하고 있다. 본 연구는 미국과 캐나다의 CBRN 교육프로그램을 조사 분석하여 우리나라 현실에 맞는 CBRN 교육훈련 프로그램을 개발하였다. 또한 한국형 화생방 교육훈련 프로그램 개발을 위하여 Activity-Action Diagram 기법을 활용 화생방 시나리오의 각 이벤트별로 대응 시 취해야 할 요구사항을 Activity로 정의하고 이의 세부적인 조치사항을 Action으로 정의하여 화생방 상황에 맞는 행동을 정의하여 실질적이고 체계적인 화생방교육훈련 프로그램을 제안함으로서 예방, 대응, 구조의 기능을 활성화하고 특히 CBRN 사태발생시 초기대응 교육훈련 프로그램을 구축하였다. 【The development of science and technology to accompany the convenience of civilization but in addition to nuclear, gas, explosion, accident and spill all over the world with the possibility of a chemical or biological terrorism response efforts collectively as a response to the urgent task of a nation. In this study major economies such as the U.S. and Canada analyzed to investigate the CBRN training programs to fit the reality in Korea CBRN training programs were developed. also the development of training programs to CBRN Korean Activity-Action Diagram technique utilized by CBRN scenarios corresponding to each event needs to be taken when the Activity is defined by its detailed definition of corrective actions for the CBRN Activity to define context-sensitive actions in particular to enable the functionality of the structure in case of CBRN emergency initial response was to establish education and training programs.】
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".