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Record W2051486324 · doi:10.1088/0954-3899/31/7/012

The shell effects in<i>s</i>-wave neutron resonance level densities ρ according to combinatorial calculations and on the basis of the semi-classical approach

2005· article· en· W2051486324 on OpenAlexfundno aff
Maria Kaczmarczyk

Bibliographic record

VenueJournal of Physics G Nuclear and Particle Physics · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsBasis (linear algebra)Resonance (particle physics)Shell (structure)SHELL modelNeutronNuclear physicsQuantum electrodynamicsAtomic physicsGeometry

Abstract

fetched live from OpenAlex

The results of calculations of level densities ρ, in the vicinity of the neutron binding energy Sn, are presented. These results were obtained using the Bohning combinatorial method for the calculation of particle-hole state densities dependent on the number of decompositions of the nucleus excitation energy to energies of independent fermions. The calculation was based on the semi-classical model description in the computation of particle-hole state densities and then of the level densities ρ, and takes into account the existence of energy gaps Δ, located near the Fermi level, in a single particle level scheme. This procedure considerably improved and extended the Bohning calculation method. The results, which were obtained in this way for ρ, for 220 nuclei, reproduce the regularities observed in the experimental values of ρ, which are dependent on the neutron number N, and they agree with the experimental data within two orders of magnitude. In addition, the neutron resonance densities ρ were calculated on the basis of the particle-hole state densities obtained using the analytical formula from Bohning's paper. To make the calculations possible, the values of 'complexity' k, as given in the semi-classical model, and the spin factors R(J), according to the paper by Ryckbosch, were used.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.368

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 head0.036
GPT teacher head0.267
Teacher spread0.231 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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