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Record W2067955101 · doi:10.2478/v10047-008-0020-8

Nuclear Theory - Nuclear Power

2008· article· en· W2067955101 on OpenAlexafffund
J. P. Svenne, L. Canton, K. S. Kozier

Bibliographic record

VenueLatvian Journal of Physics and Technical Sciences · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsAtomic Energy (Canada)University of ManitobaWinnipeg Institute for Theoretical Physics
FundersNatural Sciences and Engineering Research Council of CanadaMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsNuclear dataPhysicsNuclear physicsScatteringNeutronNucleonDeuteriumNuclear reactionNeutron scatteringNuclear powerQuantum mechanics

Abstract

fetched live from OpenAlex

Nuclear Theory - Nuclear Power The results from modern nuclear theory are accurate and reliable enough to be used for practical applications, in particular for scattering that involves few-nucleon systems of importance to nuclear power. Using well-established nucleon-nucleon (NN) interactions that fit well the NN scattering data, and the AGS form of the three-body theory, we have performed precise calculations of low-energy neutron-deuteron ( n + d ) scattering. We show that three-nucleon force effects that have impact on the low-energy vector analyzing powers have no practical effects on the angular distribution of the n + d cross-section. There appear to be problems for this scattering in the evaluated nuclear data file (ENDF) libraries, at the incident neutron energies less than 3.2 MeV. Supporting experimental data in this energy region are rather old (>25 years), sparse and often inconsistent. Our three-body results at low energies, 50 keV to 10.0 MeV, are compared to the ENDF/B-VII.0 and JENDL (Japanese Evaluated Nuclear Data Library) -3.3 evaluated angular distributions. The impact of these results on the calculated reactivity for various critical systems involving heavy water is shown.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.341

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 studies0.0000.001
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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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 designTheoretical or conceptual
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

Citations1
Published2008
Admission routes2
Has abstractyes

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