The Role of Nuclear Forensics for Determining the Origin of Nuclear Materials Out of Regulatory Control and Nuclear Security
Bibliographic record
Abstract
The international community recognizes the rise in theft and illicit trafficking of nuclear materials and radioactive sources—for malicious use and nuclear terrorism—as a serious threat. That is why a well-developed nuclear forensics capability is an integral part of a robust nuclear security program and a key element of nuclear security infrastructure. Both pre- and post-detonation nuclear forensics are vital for controlling theft and illicit trafficking of nuclear materials, as well as identifying and tracing their sources. Nuclear forensics analysis and interpretation processes for nuclear security is a systematic process that includes: (1) sample collection and categorization techniques and (2) detailed nuclear forensics analytical plans, which are a laboratory analysis of physical and chemical properties of the collected or seized nuclear and radioactive materials. Besides nuclear materials, the non-nuclear and biological materials present in seized nuclear materials can also provide important information about the source and origin of nuclear materials. Upon complete analysis of the seized materials, the data interpretation to trace the origin of the nuclear and radiological materials is one of the most critical steps to identifying the origin of the materials, which depends on the availability of similar data to compare. So, each country should have its own incident register system (IRS) and collaborate with the International Technical Working Group (ITWG), Incident and Trafficking Database (ITDB), and IAEA for data sharing and interpretation.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".