Bibliographic record
Abstract
MARVEL software was used to perform a global fit on all FeH transitions available in the literature to create an optimized and comprehensive term energy list. The diverse types of data, data sources, and electronic transitions that were used for this fit are organized and tabulated. To improve the fit, inconsistencies in the F 4 Δ -X 4 Δ analysis published in 1987 were fixed using modern computation and visualization techniques. F 4 Δ -A 4 Π transitions were identified in spectra published in the Kitt Peak archive and added to the fit. Also, several laser excitation and Fourier transform spectrometer measurements taken in our lab were added, including those from the newly discovered I 4 Φ electronic state. The final term energy list and transition list are given in the supplementary material. • Iron monohydride transitions were analysed from 16 sources plus those we measured. • 5190 transitions were inputted into MARVEL to generate 1648 energy levels. • Transition assignments from previous literature were updated when necessary. • Updated term energies are significantly more consistent with all the transitions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".