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Record W2788953207 · doi:10.1139/cjp-2017-0851

Allowed and forbidden transition rates and corresponding wavelengths for Si-like Au ion (Au<sup>65+</sup>) by relativistic configuration interaction method

2018· article· en· W2788953207 on OpenAlexvenueno aff
Safeia Hamasha, A. Almashaqba

Bibliographic record

VenueCanadian Journal of Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsAtomic physicsQuadrupoleExcited stateIonMagnetic dipoleDipoleRelativistic quantum chemistryMagnetic dipole transitionPerturbation theory (quantum mechanics)Configuration interactionQuadrupole magnetElectric dipole transitionQuantum mechanics

Abstract

fetched live from OpenAlex

Large-scale atomic calculations are carried out to produce data of atomic structure and transitions rates for Si-like Au ion (Au 65+ ). Generated atomic data are essential for modeling of M-shell spectra of gold ions in Au plasma, and fusion research. Energy levels are calculated by applying two methods: the relativistic configuration interaction method (RCI) of the flexible atomic code (FAC) and the multi-reference many body perturbation theory method (MR-MBPT). Energy levels, oscillator strengths, and transition rates are calculated for transitions between excited and ground states from n = 3l to n′l′, where n′ = 4, 5, 6, and 7; and l and l′ are the proper angular momenta of shells n and n′, respectively. The electric dipole (E1), electric quadrupole (E2), electric octupole (E3), magnetic dipole (M1), magnetic quadrupole (M2), and magnetic octupole (M3) transitions are all considered in the calculations. Correlation effects, relativistic effects, and QED effects are also included in the calculations. The two methods yield comparable values of energy levels. Data of energy levels of low-lying states and data for inner shell transitions reported in this study demonstrate good agreement with published experimental and theoretical data.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.662
Threshold uncertainty score0.596

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 head0.013
GPT teacher head0.273
Teacher spread0.260 · 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 designOther design
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

Citations6
Published2018
Admission routes1
Has abstractyes

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