Cosmic Ray Inspection and Passive Tomography for SNM Detection
Bibliographic record
Abstract
The Cosmic Ray Inspection and Passive Tomography (CRIPT) project has recently started investigating the detection of illicit Special Nuclear Material in cargo using cosmic ray muon tomography and complementary neutron detectors. We are currently performing simulation studies to help with the design of small scale prototypes. Based on the prototype tests and refined simulations, we will determine whether the muon tracking system for the full scale prototype will be based on drift chambers or extruded scintillator trackers. An analysis of the operations of the Port of Montreal has determined how long muon scan times should take if all or a subset of the cargo is to be screened. As long as the throughput of the muon system(s) is equal to the rate at which containers are unloaded from ships, the impact on port operations would not be great if a muon scanning stage were required for all cargo. We also show preliminary simulation results indicating that excellent separation between Al, Fe and Pb is possible under ideal conditions. The discrimination power is reduced but still significant when realistic momentum resolution measurements are considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".