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Record W2512894869 · doi:10.1139/cjp-2016-0317

Energy levels and radiative transitions of the K-shell excited sextet states in boron-like sulfur ion

2016· article· en· W2512894869 on OpenAlexvenueno aff
Yan Sun, Cuicui Sang, Kaikai Li, Feng Hu, Dongdong Liu, BingCong Gou

Bibliographic record

VenueCanadian Journal of Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsnot available
FundersSpecial Foundation for Theoretical Physics Research Program of ChinaNatural Science Foundation of QinghaiNational Natural Science Foundation of China
KeywordsPhysicsAtomic physicsExcited stateDipoleIonPerturbation theory (quantum mechanics)Radiative transferPolarization (electrochemistry)Quantum mechanicsPhysical chemistryChemistry

Abstract

fetched live from OpenAlex

Theoretical calculations are reported for energy levels and transition probabilities of the K-shell excited sextet series 6Se,o(m) and 6Po,e(m) (m = 1–7) for the astrophysically important element sulfur. Energy levels, fine structure splittings, and transition parameters of the high-lying sextet series 6Se,o(m) and 6Po,e(m) (m = 1–7) in boron-like sulfur ion are calculated with the multi-configuration Rayleigh–Ritz variation method. To obtain the accurate energy level, the relativistic corrections and mass polarization effect are included by using the first-order perturbation theory. Configuration structures of these sextet series are assigned according to the energies, percentage contributions of basis states to the eigenvector, relativistic effect corrections, and verification of fine structure splittings. The oscillator strengths, transition probabilities, and wavelengths of electric-dipole transitions between 6So,e(m) and 6Pe,o(m) (m = 1–7) states are also systematically calculated and discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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