Evaluation of a neutron-based active interrogation system for detection of smuggled fissionable material in packages
Bibliographic record
Abstract
Active interrogation techniques to detect and characterize special nuclear materials (SNMs) show much promise. Depending on the targeted application scenario, the techniques employ a variety of detection concepts. Neutrons or high-energy bremsstrahlung photons can be used as interrogation sources, detecting either fission gamma rays or neutrons emitted by the SNM. At Canadian Nuclear Laboratories, an active interrogation system is under development for detecting SNM smuggled in shielded packages. The system uses a deuterium – deuterium (D – D) neutron generator as the interrogation source and detects delayed neutrons using arrays of He-3 detectors. The design of the detection system was optimized using Monte Carlo simulations, and the constructed system was tested with various nuclear materials under different measurement conditions. Within a few minutes, the system is capable of detecting U-235 on the level of grams, with the possibility to distinguish between enriched and depleted uranium.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".