A test stand for off-line laser ion source development at TRIUMF
Bibliographic record
Abstract
A test stand for ion source development and laser resonance ionization spectroscopy was built and commissioned at TRIUMF. The test stand is needed to develop efficient ion sources that can function reliably in the hostile, high temperature, high radiation environment of TRIUMF's isotope separator on-line (ISOL) production target ion source. In addition, it enables laser resonance ionization spectroscopy to develop laser excitation schemes suitable for the solid-state laser systems used with TRIUMF's resonant ionization laser ion source . Also, it allows for possible improvement of current ion sources and validation of new designs. The test stand employs a copy of the ion optics used on-line, so that results can be transferred directly to radioactive ion beam production. Due to space restrictions and the need for rapid mass scans, a quadrupole mass spectrometer is used as a mass separator. One of the first experiments conducted on the laser ion source test stand (LIS STAND) was resonant ionization spectroscopy of gallium to improve on the ionization scheme previously used on-line, so that low yield isotopes (e.g., (62)Ga) become available for experiments. Different Rydberg series in gallium were observed and autoionizing states were searched for. The overall LIS STAND system performance, characteristics, and the first resonant ionization spectroscopy are described.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".