Contrast Analysis for CBRN attacks on educational research and best practices
Bibliographic record
Abstract
This study is to protect peoples' life, minimize the property damage by coping with threats quickly and take more preventive measures in advance against nuclear bomb, CBR, and potential explosive. For this, CBRNE(Chemical, Biological, Radiological, Nuclear, Explosive) program research was used. Thanks to advance in technology, terrorist groups and even individuals make or keep nuclear and CBR weapons. And also it's likely that disaster and threats from a toxic gas, acute pathogens, accidents in the nuclear power plants and a high explosive could be happened a lot. Recently more organized terrorist groups maintain random attacks for unspecified individuals and also it's highly likely that a large-scale terrorist attack by WMD and CBRNEwill be done. To take strict measures against CBRNE attacks by terrorists is on the rise as an urgent national task. Moreover biological weapons are relatively easy and inexpensive to obtain or produce and cause mass casualties with a small amount. For this reason, more than 25 countries have already possessed them. In the 21 st century, the international safety environment marks the age of complicated threats transnational threats such as comprehensive security and terror, organized crime, drug smuggling, illegal trade of weapons of mass destruction, and environmental disruption along with traditional security threats. These cause military threats, terror threats, and CBRNE threats in our daily life to grow. Therefore it needs to come up with measures in such areas as research development, policy, training program. Major industrial nations on CBRNE like USA, Canada, Switzerland, and Israel have implemented various educational programs. These researches could be utilized as basic materials for drawing up plans for civil defense, emergency services and worldwide countermeasures against CBRNE.
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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 | 0.006 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".