Bibliographic record
Abstract
Abstract Nuclear forensics is the interdisciplinary branch of forensic science that deals with the analysis and examination of nuclear or other radioactive materials, or of evidence that is contaminated with radionuclides in the context of legal proceedings under international or national law related to nuclear security. To control and avoid the threat of terrorism posed by illicit trafficking and mismanagement, nuclear and other radioactive materials must be strictly regulated. Thus, the goal of nuclear forensic analysis is to discover what radioactive materials were confiscated, how, when, and where they were manufactured, and what their intended applications were. Nuclear forensic scientist has a wide array of analytical tools to use for detecting signatures in radioactive materials. These individual techniques can be sorted into three broad categories: bulk analysis tools, imaging tools, and micro-analysis tools. Particular interest in nuclear forensics is particle morphology, isotopic composition of a material, presence of impurities, and microstructure. These properties can vary between materials of different origins due to varying processing or geological conditions, thus, allowing for discrimination of material history and prediction of provenance. This review article presents many key analytical techniques and discusses the main application and challenges of the most common techniques currently used in nuclear forensics analysis.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.019 |
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".