We CANS do it: would a SANS on PC-CANS be worth it?
Bibliographic record
Abstract
The proposed prototype Canadian Compact Accelerator-driven Neutron Source (PC-CANS) presents a cost-effective solution to address the shrinking global availability of neutron beams. Designed to support a range of applications, including Small Angle Neutron Scattering (SANS), neutron imaging, boron neutron capture therapy, and radioisotope production, PC-CANS aims to restore and enhance Canada’s neutron science capabilities. Benchmarking against the LOQ instrument at the ISIS Neutron and Muon Source demonstrates that a PC-CANS SANS, despite operating with lower neutron flux, would provide comparable data quality for a variety of scientifically interesting samples with only modest increases in measurement duration. A set of well-characterized samples—including lipid bilayers for membrane structure and lipid domain monitoring, as well as scattering standards such as meso-porous silicalite and silver behenate—were measured on LOQ under both standard and reduced-flux configurations. The initial results show that with only an approximate sixfold increase in acquisition time, data was reproducible at a usable quality. This suggests that an SANS instrument at PC-CANS will be a valuable tool for a wide range of scientific fields, from material science to biophysics. With ongoing improvements, ranging from technological advances to AI-driven data collection, PC-CANS has the potential to become a key resource for Canadian researchers and further foster international scientific collaboration.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".