Configuration interaction in statistically complete hybrid-structure atomic models<sup>1</sup>This article is part of a Special Issue on the 10th International Colloquium on Atomic Spectra and Oscillator Strengths for Astrophysical and Laboratory Plasmas.
Bibliographic record
Abstract
Configuration interaction can have significant effects on the transition energies and strengths of diagnostically important X-ray emission lines and features. However, including full configuration interaction (CI) effects by computing extensive collections of fine structure (LS term) levels may be computationally prohibitive for complex ions. In this paper, we show that CI effects in a simple ion vary little with the configuration of spectator electrons, and that CI effects in complex ions are fairly consistent from one ionization stage to the next, particularly for highly charged ions. Therefore, we argue that the CI effects within an ion can be approximated by extending the CI effects computed from a small subset of configurations in that ion to all transitions of the type (nlj)–(nlj)′ in that ion, regardless of the spectator electron. This approach to CI enforces consistency between the fine structure and averaged states in hybrid-structure atomic models, which are designed to provide a computationally efficient balance of spectroscopic accuracy and statistical completeness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".