Recent progress in spectroscopy of tungsten<sup>1</sup>This review 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
This contribution reviews experimental and theoretical work on spectroscopy of tungsten published since the last critical compilation of the energy levels and spectral lines of highly ionized tungsten (Kramida and Shirai. At. Data Nucl. Data Tables, 95, 305 (2009)). Since then, 18 new experimental studies were published, which resulted in new identifications and (or) significantly improved wavelengths of spectral lines and energy levels of Li-like through As-like and Pm-like tungsten. A few tens of theoretical studies of tungsten spectra were published since 2008. A number of them report on high-precision calculations of energy levels, transition wavelengths, and radiative rates for tungsten spectra, such as neutral tungsten, Yb-like, Rh-like through Rb-like, Ag-like, Ga-like, Zn-like, Ni-like, Ca-like, Al-like, Mg-like, Na-like, Ne-like, B-like, Be-like, and Li-like. These developments are reviewed. Based on new experimental data, systematic errors are removed from some of the earlier measurements. Some new data are obtained by analyzing publications of other authors. Based on new published theoretical data, some old experimental results were confirmed and assessed. Revised and extended tables of energy levels and spectral lines of highly ionized tungsten are presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".