Bibliographic record
Abstract
For the acceleration of radioactive ions the usable mass range is limited by the A/q acceptance of the first accelerator stage. Since an efficient primary ion source normally produces singly charged ions, charge state breeding is necessary if higher masses are to be accelerated. At TRIUMF an ECR source has been chosen as a breeder due to its potential high efficiency in producing intermediate A/q values. To minimize the necessity for further stripping an A/q around 6 is desirable. A 14 GHz “PHOENIX” booster from Pantechnik has been set up on a test bench. The singly charged ions are produced from different ion sources, which can be mounted in a standard ISAC target‐ion‐source set‐up. For the first tests an ECR source to produce noble gas beams has been chosen. The aim of the measurements at the test bench is to find the optimum operation conditions of the charge state booster and the injection and extraction ion optics. Working with radioactive ions always means that the system should aim for high efficiency, as the production of such species is limited. Therefore, special emphasis has to be put on the highest yield for the production of the desired charge state. A second point is the analysis of the extracted beam quality in order to optimize mass separation and transport efficiency. The paper shows the status of the set‐up and reports on first results of the charge breeding of Ar, Ne and Xe. With Xe a total efficiency of 22.5 % has been achieved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".