NON-DESTRUCTIVE EXAMINATION USING NEUTRONS: A NUCLEAR WASTE AND ORPHANED SOURCE CHARACTERIZATION CASE STUDY APPLICABLE TO NUCLEAR FORENSICS
Bibliographic record
Abstract
Found unknown radioactive material is often contained in a package so the chemical and physical form of the material itself is unknown, and the detail of the packaging is likewise unknown. Together, these present a significant risk on how to handle the package for destructive examination of its contents. Whether of nefarious origin or the result of less stringent practices of yesteryear the material needs to be properly identified and characterized for appropriate disposition. Results of neutron imaging, neutron diffraction, and delayed neutron analysis as applied to an examination of sealed capsules containing unknown radioactive materials are presented. The results demonstrate that neutron-based non-destructive examination techniques can be employed for inspecting encapsulated radioactive samples to identify the materials, to elucidate the internal physical structure of the radioactive material and encapsulation, and to estimate the mass of fissile and fissionable materials within. This characterization of orphaned radioactive special nuclear material illustrates the potential for these techniques in nuclear forensics investigations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".