Application of stimulated Raman pumping toward the first study of chemical reaction dynamics of the muonium atom with H<sub>2</sub>*
Bibliographic record
Abstract
Abstract The chemical reaction of interest, from the field of μSR spectroscopy, is that of the muonium atom (Mu) with molecular hydrogen in its first vibrational excited state. The stimulated Raman pumping (SRP) technique has been used to prepare H2 in its ν = 1 state in a series of off‐line experiments at the LASIR Lab at UBC. Three different SRP excitation schemes employing the second harmonic of a Nd:YAG laser have been analysed in connection with this application, for 5.5, 13, and 26 cm‐long reaction cells. The laser energy varied from ∼80 to ∼550 mJ/pulse (or from ∼0.07 GW/cm2 to ∼0.46 GW/cm2) at a 10 Hz repetition rate. The importance of injection seeding of the Nd:YAG laser for efficient molecular excitation is shown. The major challenge of this experiment was to ensure a homogeneous excitation over a large volume (few cm3) while achieving a high amount of vibrationally excited H2 (3‐15 Torr). This is demanded by the requirements of superposition of a pulsed muon beam with the laser beam and the expected rate constant of the Mu + H2*(ν=1) →MuH + H reaction. In this endeavour it was also of interest to explore the effect of added moderator gas to the hydrogen and so the effect of Ar/Xe moderator on the efficiency of hydrogen pumping for different SRP excitation schemes is also shown. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.000 | 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) | 0.001 | 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".