Atomic Data Assessment with PyNeb: Radiative and Electron Impact Excitation Rates for [Fe ii] and [Fe iii]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it