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Record W2035639760 · doi:10.1063/1.2378632

The permanent electric dipole moments and magnetic g factors of uranium monoxide

2006· article· en· W2035639760 on OpenAlexafffund
Michael C. Heaven, Vasiliy Goncharov, Timothy C. Steimle, Tongmei Ma, C. Linton

Bibliographic record

VenueThe Journal of Chemical Physics · 2006
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaArizona State University
KeywordsZeeman effectStark effectDipoleMonoxideAtomic physicsChemistryGround stateElectronic structureElectric fieldSpectral lineMagnetic momentElectric dipole transitionMagnetic dipoleCondensed matter physicsPhysicsMagnetic fieldComputational chemistryInorganic chemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Permanent electric dipole moments and magnetic g factors for uranium monoxide (UO) have been determined from analyses of optical Stark and Zeeman spectra recorded at a spectral resolution that approaches the natural linewidth limit. Numerous branch features in the previously characterized [L. A. Kaledin et al., J. Mol. Spectrosc. 164, 27 (1994)] (0,0) [18403]5-X(1)4 and (0,0) [18404]5-X(1)4 electronic transitions were recorded in the presence of tunable static electric (Stark effect) or magnetic (Zeeman effect) fields. The lines exhibited unusually large Zeeman tuning effects. A ligand field model and an ab initio electronic structure calculation [R. Tyagi, Ph.D. thesis, The Ohio State University (2005)] were used to interpret the ground state properties. The results indicate that the low energy electronic states of UO are sufficiently ionic for the meaningful application of ligand field theory models. The dipole moments and g factors were distinctly different for the three electronic states examined, which implies that these properties may be used to deduce the underlying electronic state configurations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.275

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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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 designBench or experimental
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

Citations33
Published2006
Admission routes2
Has abstractyes

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