Atomic data for Ne-like ions useful in plasma diagnostic
Bibliographic record
Abstract
Motived by the recent measurement of transition lines for Ne-like Hf and W, we have reported atomic data in the form of multiconfiguration Dirac–Fock transition energies and wavefunction compositions of 209 levels belonging to the configurations 2s22p6, 2s22p5ns (n = 3, 4, 5, 6, 7), 2s22p5np (n = 3, 4, 5, 6, 7), 2s22p5nd (n = 3, 4, 5, 6, 7), 2s22p5nf (n = 4, 5), 2s22p55g, 2s2p6ns (n = 3, 4, 5), 2s2p6np (n = 3, 4, 5), 2s2p6nd (n = 3, 4, 5), 2s2p6nf (n = 4, 5), and 2s2p65g of Hf LXIII, Ta LXIV, W LXV, and Re LXVI. Radiative rates, oscillator strengths, transition wavelengths, and line strengths have been calculated for ground state electric dipole (E1) transition among these levels. These values were obtained using GRASP (general-purpose relativistic atomic structure package) code, which includes Breit and QED effects along with Dirac–Fock potential and second-order Coulomb interaction. We have compared our results with the data compiled using FAC (flexible atomic code) and also with the recent results available in the literature. The accuracy of the data is assessed. We predict new energy levels, oscillator strength, and transition probability data, where no other theoretical or experimental results are available, which will form the basis for future experimental work.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".