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Record W2920258488 · doi:10.1139/cjp-2018-0697

Effects of the isoscalar and isovector interaction on the ground-state magnetic moments of odd-mass <sup>137–145</sup>Ce nuclei

2019· article· en· W2920258488 on OpenAlexvenueno aff
H. Yakut, E. Tabar, G. Hoşgör

Bibliographic record

VenueCanadian Journal of Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsnot available
Fundersnot available
KeywordsIsovectorPhysicsIsoscalarQuasiparticleNeutronMagnetic momentAtomic physicsGround statePolarization (electrochemistry)Nuclear physicsCondensed matter physicsNucleonSuperconductivity

Abstract

fetched live from OpenAlex

A systematic study of the magnetic properties of deformed odd-neutron 137–145Ce isotopes using the microscopic quasiparticle phonon nuclear model (QPNM) has been presented. The QPNM includes residual spin–spin interaction in both isoscalar and isovector channels. The analysis shows that in the isoscalar channel contributions to the magnetic moment coming from the neutron and proton systems practically cancel out each other. On the other hand, in the isovector channel, the coherent contribution coming from the quasiparticle–phonon interactions leads to a spin polarization (core polarization), which is important for determination of the quenched spin gyromagnetic factors (gs). The quenched spin gyromagnetic factors so called [Formula: see text] have been found to range from [Formula: see text] to [Formula: see text] in the odd-mass 137–145Ce isotopic chain, which is similar to its phenomenological value ([Formula: see text] between [Formula: see text] and [Formula: see text]). By taking into consideration the core polarization effects, the available experimental data are satisfactorily reproduced with an accuracy of 0.01μN–0.1μN.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes1
Has abstractyes

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