Sparse-view muon computed tomography of an operating research reactor
Bibliographic record
Abstract
Directional radiography maps and projected computed tomography images of the ZED-2 research reactor facility at Canadian Nuclear Laboratories (CNL) have been reconstructed using a sparse, asymmetric, and time-restricted set of muon flux measurements. The accuracy of the radiography maps in representing the dominant infrastructure surrounding each detector position confirms MuPIC’s radiography capabilities for the first time in the field. Computed tomography images are reconstructed using variants of a least squares conjugate gradient technique, the Simultaneous Iterative Reconstruction Technique (SIRT), and the L1NORM technique, of which SIRT variants are found to be the most stable overall. Despite significant artifacts due to the sparsity of the data set, the dominant features of the reactor well are visible in the computed tomography projections—including reactor well walls, the graphite calandria, and fuel structure—further providing a first field validation of CNL’s computed tomography algorithm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".