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Record W2739559055 · doi:10.12943/cnr.2017.00002

APPLICATION OF GEANT4 TO THE DATA ANALYSIS OF THERMAL NEUTRON SCATTERING EXPERIMENTS

2017· article· en· W2739559055 on OpenAlex
Gang Li, G. Bentoumi, Z. Tun, Liqian Li, B. Sur

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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCNL Nuclear Review · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsCanadian Nuclear Laboratories
FundersCanadian Nuclear Laboratories
KeywordsScatteringNeutron scatteringSmall-angle neutron scatteringNeutronNeutron temperaturePhysicsNuclear physicsCross section (physics)Scattering lengthBiological small-angle scatteringNeutron cross sectionMaterials scienceOptics

Abstract

fetched live from OpenAlex

Multiple scattering has been well recognized as an important correction in neutron scattering cross-section measurements. The GEANT4 simulation toolkit includes a special thermal neutron scattering model and a corresponding data library at low neutron energies (<4 eV). A new method using GEANT4 to estimate the multiple-scattering effect in thermal neutron scattering experiments is presented. The method was applied to the double differential cross-section measurements of light water with various sample thicknesses under ambient conditions of temperature and pressure. The resulting scattering law for neutron energy transfer from 42.0 to 14.6 meV over scattering angles from 10° to 110° is presented and compared with the tabulated Evaluated Nuclear Data File (ENDF/B-VII).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.239

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 science0.0010.000
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 head0.050
GPT teacher head0.351
Teacher spread0.301 · 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