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Record W2979862306 · doi:10.1139/facets-2019-0003

Seventy years of scientific impact using neutron beams at the Chalk River Laboratories

2019· article· en· W2979862306 on OpenAlexafffundvenueabout
Daniel Banks, Thad A. Harroun

Bibliographic record

VenueFACETS · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsBrock University
FundersOak Ridge National LaboratoryCanadian Light Source
KeywordsContext (archaeology)NeutronResearch reactorNeutron scatteringClosure (psychology)Nuclear physicsPhysicsNuclear engineeringEnvironmental sciencePolitical scienceGeographyEngineeringArchaeologyLaw

Abstract

fetched live from OpenAlex

The 31 March 2018 closure of the National Research Universal reactor marked the end of over 70 years of materials research using neutron beams from major neutron sources at the Chalk River Laboratories in Chalk River, Ontario, Canada. This closure will have a major impact on the Canadian materials research community, including researchers in the physics, chemistry, and engineering of materials. After a brief review of the history of neutron beams at the Chalk River Laboratories, we present the results of a bibliometric study of the scientific output of the research with neutron beams. In this study, we compiled a complete bibliographic record of the research papers beginning with the first neutron scattering experiments at the National Research Experimental reactor in 1947, analyzed the citations from 1980 onward, and benchmarked the results against major neutron beam facilities in other countries and against other major research facilities in Canada. We also conducted a broader bibliometric analysis of the use of neutron scattering data among all Canadians, regardless of where the data were taken. The results provide a useful metric of the size of the Canadian neutron scattering community and places into context the importance of access to this research tool.

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.

How this classification was reachedexpand

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.477

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)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.264
Teacher spread0.254 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes4
Has abstractyes

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