Neutrino-based safeguards of CANDU spent fuel using superconducting detectors and the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>CE</mml:mi><mml:mi>ν</mml:mi><mml:mi>NS</mml:mi></mml:mrow></mml:math> interaction
Bibliographic record
Abstract
To prevent the unauthorized spread of radioactive materials, it is essential to detect and monitor spent nuclear fuel. This paper investigates the feasibility of using a detector based on transition edge superconductors to monitor spent Canada deuterium uranium fuel in two distinct scenarios. The first of these considered the monitoring of a CANSTOR container at the location of the Gentilly-2 nuclear power plant. The fuel in a CANSTOR container is contained within baskets, which are then stored in tubes in the container. An individual detector with a mass of 1 kg is not sensitive to the removal of a single basket from the container without an unfeasibly long monitoring time. When an entire tube in the container is emptied (equivalent to approximately 5% of the fuel in the container), the results are improved. The second scenario examined the feasibility of monitoring a single dry storage container (DSC) at the Pickering site. The DSCs are much smaller than a CANSTOR container and contain approximately 7.4 tonnes of spent fuel. The background due to the neutrinos from the nearby reactors at the Pickering site was also evaluated. It was found that monitoring DSC was unfeasible due to the high reactor neutrino background. Both studies utilized fuel that was 30 years old; monitoring a container loaded with spent fuel that is younger would require a shorter monitoring time due to the fuel's higher radioactivity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".