Managing Neutron Beam Scans at the Canadian Neutron Beam Centre
Bibliographic record
Abstract
The Canadian Neutron Beam Centre (CNBC) of the Canadian Nuclear Laboratories (CNL) operate six beam lines for material research. A single beam line experiment requires scientists to acquire data as a sequence of scans that involves data acquisition at many points, varying sample positions, samples, wavelength, sample environment, etc. The points at which measurements must be taken can number in the thousands with scans or their variations having to be run multiple times. At the CNBC an approach has been developed to allow scientists to specify and manage their scans using a set of processes and tools. Scans are specified using a set of constructors and a scan algebra that allows scans to be combined using a set of scan operators. Using the operators of the algebra, complex scan sequences can be constructed from simpler scans and run unattended for up to a few days. Based on the constructors and the algebra, tools are provided to scientists to build, organize and execute their scans. These tools can take the form of scripting languages, spreadsheets, or databases. This scanning technique is currently in use at CNL, and has been implemented in Python on an EPICS based control system.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".