Atomic Data Assessment with PyNeb: Radiative and Electron Impact Excitation Rates for [Fe ii] and [Fe iii]
Bibliographic record
Abstract
We use the PyNeb 1.1.16 Python package to evaluate the atomic datasets available for the spectral modeling of [Fe ii] and [Fe iii], which list level energies, A-values, and effective collision strengths. Most datasets are reconstructed from the sources, and new ones are incorporated to be compared with observed and measured benchmarks. For [Fe iii], we arrive at conclusive results that allow us to select the default datasets, while for [Fe ii], the conspicuous temperature dependency on the collisional data becomes a deterrent. This dependency is mainly due to the singularly low critical density of the 3d7a4F9/2 metastable level that strongly depends on both the radiative and collisional data, although the level populating by fluorescence pumping from the stellar continuum cannot be ruled out. A new version of PyNeb (1.1.17) is released containing the evaluated datasets.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".