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Managing Neutron Beam Scans at the Canadian Neutron Beam Centre

2015· article· en· W2558602049 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJACOW · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsCanadian Nutrition Society
Fundersnot available
KeywordsNeutronBeam (structure)Neutron radiationNuclear physicsPhysicsOptics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.012
GPT teacher head0.225
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it