A CRDS sputter-source experiment to study MH radicals: application to NiH and NiD
Bibliographic record
Abstract
Signatures of metal hydride molecules appear in the optical spectra of cool stars. The observed spectra are used not only for identification of the molecule, but also to assess the abundance of the metal from which the molecule is composed, and to measure the strength of the magnetic field in which the molecule is immersed through the Zeeman splitting of individual spectral lines. Metal hydrides are short-lived radicals, often produced via an electrical discharge, and their steady-state concentrations in a sample are low. High-sensitivity probing techniques, like laser-induced fluorescence, are often appropriate, but (typically much less sensitive) absorption techniques are more useful to assess metal abundances. We describe here a cavity ring-down spectroscopy experiment, usually used to detect absorptions from stable molecules, to collect spectra with very high sensitivity and reproducibility from prototypical metal hydrides NiH and NiD. We have constructed an optical cavity of high finesse (F = 60,000) into which a sputtering source is inserted, and have employed optical fibre and a rigid mounting scheme to keep the ring-down mirrors in alignment during an experiment and between days. We compare our NiH/NiD absorption data with literature results, and highlight some of the strengths and weaknesses of this approach.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".